Protein Conformational Sampling Research Articles

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.

Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system.

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

MDSubSampler: a posteriori sampling of important protein conformations from biomolecular simulations.

Molecular dynamics (MD) simulations have become routine tools for the study of protein dynamics and function. Thanks to faster GPU-based algorithms, atomistic and coarse-grained simulations are being used to explore biological functions over the microsecond timescale, yielding terabytes of data spanning multiple trajectories, thereby extracting relevant protein conformations without losing important information is often challenging. We present MDSubSampler, a Python library and toolkit for a posteriori subsampling of data from multiple trajectories. This toolkit provides access to uniform, random, stratified, weighted sampling, and bootstrapping sampling methods. Sampling can be performed under the constraint of preserving the original distribution of relevant geometrical properties. Possible applications include simulations post-processing, noise reduction, and structures selection for ensemble docking. MDSubSampler is freely available at https://github.com/alepandini/MDSubSampler, along with guidance on installation and tutorials on how it can be used.

Discovery of a PDZ Domain Inhibitor Targeting the Syndecan/Syntenin Protein-Protein Interaction: A Semi-Automated "Hit Identification-to-Optimization" Approach.

The rapid identification of early hits by fragment-based approaches and subsequent hit-to-lead optimization represents a challenge for drug discovery. To address this challenge, we created a strategy called "DOTS" that combines molecular dynamic simulations, computer-based library design (chemoDOTS) with encoded medicinal chemistry reactions, constrained docking, and automated compound evaluation. To validate its utility, we applied our DOTS strategy to the challenging target syntenin, a PDZ domain containing protein and oncology target. Herein, we describe the creation of a "best-in-class" sub-micromolar small molecule inhibitor for the second PDZ domain of syntenin validated in cancer cell assays. Key to the success of our DOTS approach was the integration of protein conformational sampling during hit identification stage and the synthetic feasibility ranking of the designed compounds throughout the optimization process. This approach can be broadly applied to other protein targets with known 3D structures to rapidly identify and optimize compounds as chemical probes and therapeutic candidates.

Dynamical activation of function in metalloenzymes.

Formulations of hydrogen tunneling in enzyme-catalysed C-H activation reactions indicate enthalpic barriers to reaction that are independent of chemical steps and dependent on the protein scaffold. A tool to identify catalytically relevant site-specific protein thermal networks has emerged from temperature-dependent hydrogen deuterium exchange (TDHDX). Focusing on mutant enzyme forms with altered activation energies for catalysis, TDHDX provides a comparative analysis of the impact of mutation on Ea for local protein unfolding. Identified thermal networks appear unrelated to protein scaffold conservation and track to the dictates of the catalysed reaction, including sites for metal binding. The positions of thermal networks provide a framework for further understanding of time-dependent, functionally relevant protein motions. Measurement of nanosecond Stokes shifts at the surface of the thermal network in soybean lipoxygenase yields activation energies that are identical to Ea values measured for kcat . This finding identifies a rapid (> nanosecond), long-range and cooperative structural reorganization as the thermal barrier to catalysis. A model for protein dynamics is put forward that integrates broadly distributed protein conformational sampling with protein embedded thermal networks.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins.

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.

Using PD-L1 full-length structure, enhanced induced fit docking and molecular dynamics simulations for structural insights into inhibition of PD-1/PD-L1 interaction by small-molecule ligands

ABSTRACT T-cell activation through the blockade of PD-1/PD-L1 interaction by monoclonal antibodies has demonstrated the clinical benefit for patients with diverse types of metastatic cancers. However, a number of limitations when producing and using antibodies hamper their broader clinical application. This awakened a significant interest in design and discovery of small-molecule inhibitors (SMIs) of PD-1/PD-L1 interaction, and several such inhibitors have been identified. However, up to now, many mechanistic details of their inhibitory action remain not fully understood. In this work, a new protocol of enhanced protein conformational sampling combining RosettaLigand and Schrodinger Induced Fit Docking approaches with Molecular Dynamics simulations, preceded by structural modelling of the full-length PD-L1 – dimer in the heterogeneous environment including the membrane and extracellular solution, are used for the prediction of atomistic structures of the complexes of the PD-L1 dimers with BMS-1016, BMS-2007, BMS-4121, BMS-40210, four SMIs with a high inhibitory activity in vitro against PD-1/PD-L1 interaction. Critical protein–ligand interactions responsible for the stabilisation of the complexes of the PD-L1 dimer with SMIs and high inhibitory activities of these SMIs against the PD-1/PD-L1 interaction are revealed. The roles of the membrane and of interaction of PD-L1 with its cis and trans protein partners are also addressed.

Protein Dynamics to Define and Refine Disordered Protein Ensembles.

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R2 relaxation rates and Lipari-Szabo order parameters (S2 values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Impact of Increased Membrane Realism on Conformational Sampling of Proteins.

The realism and accuracy of lipid bilayer simulations through molecular dynamics (MD) are heavily dependent on the lipid composition. While the field is pushing toward implementing more heterogeneous and realistic membrane compositions, a lack of high-resolution lipidomic data prevents some membrane protein systems from being modeled with the highest level of realism. Given the additional diversity of real-world cellular membranes and protein-lipid interactions, it is still not fully understood how altering membrane complexity affects modeled membrane protein functions or if it matters over long-timescale simulations. This is especially true for organisms whose membrane environments have little to no computational study, such as the plant plasma membrane. Tackling these issues in tandem, a generalized, realistic, and asymmetric plant plasma membrane with more than 10 different lipid species is constructed herein. Classical MD simulations of pure membrane constructs were performed to evaluate how altering the compositional complexity of the membrane impacted the plant membrane properties. The apo form of a plant sugar transporter, OsSWEET2b, was inserted into membrane models where lipid diversity was calculated in either a size-dependent or size-independent manner. An adaptive sampling simulation regime validated by Markov-state models was performed to capture the gating dynamics of OsSWEET2b in each of these membrane constructs. In comparison to previous OsSWEET2b simulations performed in a pure POPC bilayer, we confirm that simulations performed within a native-like membrane composition alter the stabilization of apo OsSWEET2b conformational states by ∼1 kcal/mol. The free-energy barriers of intermediate conformational states decrease when realistic membrane complexity is simplified, albeit roughly within sampling error, suggesting that protein-specific responses to membranes differ due to altered packing caused by compositional fluctuations. This work serves as a case study where a more realistic bilayer composition makes unbiased conformational sampling easier to achieve than with simplified bilayers.

A Minireview on Temperature Dependent Protein Conformational Sampling.

In this minireview we discuss the role of the more subtle conformational change-protein conformational sampling and connect it to the classic relationship of protein structure and function. The theory of pre-existing functional states of protein are discussed in context of alternate protein conformational sampling. Last, we discuss how temperature, ligand binding and mutations affect the protein conformational sampling mode which is linked to the protein function regulation. Thereviewincludes several protein systems that showed temperature dependent protein conformational sampling. We also specifically included two enzyme systems, thermophilic alcohol dehydrogenase (ht-ADH) and thermolysin which we previously studied when discussing temperature dependent protein conformational sampling.

Thermal Adaptation of Enzymes: Impacts of Conformational Shifts on Catalytic Activation Energy and Optimum Temperature.

Thermal adaptation of enzymes is essential for both living organism development in extreme conditions and efficient biocatalytic applications. However, the molecular mechanisms leading to a shift in catalytic activity optimum temperatures remain unclear, and there is increasing experimental evidence that thermal adaptation involves complex changes in both structural and reactive properties. Here, a combination of enhanced protein conformational sampling with an explicit chemical reaction description was applied to mesophilic and thermophilic homologues of the dihydrofolate reductase enzyme, and a quantitative description of the stability and catalytic activity shifts between homologues was obtained. The key role played by temperature-induced shifts in protein conformational distributions is revealed. In contrast with pictures focusing on protein flexibility and dynamics, it is shown that while the homologues' reaction free energies are similar, the striking discrepancy between their activation energies is caused by their different conformational changes with temperature. An analytic model is proposed that combines catalytic activity and structural stability, and which quantitatively predicts the shift in homologue optimum temperatures. It is shown that this general model provides a molecular explanation of changes in optimum temperatures for several other enzymes.

Development of New Methods for Enhanced Conformational Sampling of GPCRs

G protein coupled receptors (GPCRs) are integral membrane proteins that enable a cell to respond to extracellular stimuli like light, small molecules, peptides, and proteins. These receptors have evolved as highly flexible proteins that possess multiple functionally important conformational states, which enable these receptors to activate pleiotropic signaling events inside the cells. Recent progress in membrane protein structural biology techniques and cryo‐electron microscopy is providing structural evidence of two or more distinct receptor conformations for these receptors. Computational structural modeling approaches can complement and supplement these studies by predicting all functionally important states of a receptor, however, they run into the classical protein conformational sampling problem. We are developing a computational biophysical method called Enhanced Conformational Markov‐state Sampling in Membrane BiLayer Environment (EnCoMSeMBLE) to solve this sampling problem for GPCRs. We are utilizing Markov‐State‐Models to combine the Boltzmann sampling of receptor conformations from molecular dynamics (MD) with the brute‐force sampling of receptor conformations from our previously developed ActiveGEnSeMBLE method. The method was applied to the A2A receptor starting from the active state by only using traditional MD simulations. This is not surprising because the pre‐activce states is a downhill direction on the energy landscape. However, applying both MD and ActivateGEnSeMBLE methods was able to predict pre‐active states from the inactive states which, classical MD cannot achieve. Our results indicated that our methods provide an unbiased method to sample the conformational landscape. This can protentional sample unexplored regions of the conformational landscape and find novel conformations that can elucidate the mechanistic properties that GPCRs possess.Support or Funding InformationThis project was funded through a grant from the National Institutes of Health (NIH) Building Infrastructure Leading to Diversity (BUILD) #5TL4GM118977 or # 5RL5GM118975.

Conformational sampling and kinetics changes across a non-Arrhenius break point in the enzyme thermolysin.

Numerous studies have suggested a significant role that protein dynamics play in optimizing enzyme catalysis, and changes in conformational sampling offer a window to explore this role. Thermolysin from Bacillus thermoproteolyticus rokko, which is a heat-stable zinc metalloproteinase, serves here as a model system to study changes of protein function and conformational sampling across a temperature range of 16–36 °C. The temperature dependence of kinetics of thermolysin showed a biphasic transition at 26 °C that points to potential conformational and dynamic differences across this temperature. The non-Arrhenius behavior observed resembled results from previous studies of a thermophilic alcohol dehydrogenase enzyme, which also indicated a biphasic transition at ambient temperatures. To explore the non-Arrhenius behavior of thermolysin, room temperature crystallography was applied to characterize structural changes in a temperature range across the biphasic transition temperature. The alternate conformation of side chain fitting to electron density of a group of residues showed a higher variability in the temperature range from 26 to 29 °C, which indicated a change in conformational sampling that correlated with the non-Arrhenius break point.

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data.

Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning.

It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Because of this rarity, few methods have been developed for predicting cis isomers from protein sequences, most of which are based on outdated datasets and lack the means for independent testing. In this work, using a database of >10000 high-resolution protein structures, we update the statistics of cis isomers and develop a sequence-based prediction technique using an ensemble of residual convolutional and long short-term memory bidirectional recurrent neural networks that allow learning from the whole protein sequence. We show that ensembling eight neural network models yields maximum Matthews correlation coefficient values of approximately 0.35 for cis-Pro isomers and 0.1 for cis-nonPro residues. The method should be useful for prioritizing functionally important residues in cis isomers for experimental validations and improving the sampling of rare protein conformations for ab initio protein structure prediction.

Defining Low-Dimensional Projections to Guide Protein Conformational Sampling.

Exploring the conformational space of proteins is critical to characterize their functions. Numerous methods have been proposed to sample a protein's conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large proteins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner's performance are still poorly understood issues. In this article, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein's flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling and (ii) automatically constructing such projections is a reasonable alternative.

Refined Parameterization of Nonbonded Interactions Improves Conformational Sampling and Kinetics of Protein Folding Simulations.

Recent advances in computational technology have enabled brute-force molecular dynamics (MD) simulations of protein folding using physics-based molecular force fields. The extensive sampling of protein conformations afforded by such simulations revealed, however, considerable compaction of the protein conformations in the unfolded state, which is inconsistent with experiment. Here, we show that a set of surgical corrections to nonbonded interactions between amine nitrogen-carboxylate oxygen and aliphatic carbon-carbon atom pairs can considerably improve the realism of protein folding simulations. Specifically, we show that employing our corrections in ∼500 μs all-atom replica-exchange MD simulations of the WW domain and villin head piece proteins increases the size of the denatured proteins' conformations and does not destabilize the native conformations of the proteins. In addition to making the folded conformations a global minimum of the respective free energy landscapes at room temperature, our corrections also make the free energy landscape smoother, considerably accelerating the folding kinetics and, hence, reducing the computational expense of a protein folding simulation.

UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling.

Motivation: Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling.Results: Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP.Availability and Implementation: Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/.Contact: chengji@missouri.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design.

Computer-aided drug design plays an important role in medicinal chemistry to obtain insights into molecular mechanisms and to prioritize design strategies. Although significant improvement has been made in structure based design, it still remains a key challenge to accurately model and predict induced fit mechanisms. Most of the current available techniques either do not provide sufficient protein conformational sampling or are too computationally demanding to fit an industrial setting. The current study presents a systematic and exhaustive investigation of predicting binding modes for a range of systems using PELE (Protein Energy Landscape Exploration), an efficient and fast protein-ligand sampling algorithm. The systems analyzed (cytochrome P, kinase, protease, and nuclear hormone receptor) exhibit different complexities of ligand induced fit mechanisms and protein dynamics. The results are compared with results from classical molecular dynamics simulations and (induced fit) docking. This study shows that ligand induced side chain rearrangements and smaller to medium backbone movements are captured well in PELE. Large secondary structure rearrangements, however, remain challenging for all employed techniques. Relevant binding modes (ligand heavy atom RMSD < 1.0 Å) can be obtained by the PELE method within a few hours of simulation, positioning PELE as a tool applicable for rapid drug design cycles.