Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Collective Variable for Metadynamics Derived From AlphaFold Output.

ABSTRACT: AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and β hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.

SUBMITTER: Spiwok V

PROVIDER: S-EPMC9234394 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Collective Variable for Metadynamics Derived From AlphaFold Output.

Spiwok Vojtěch V Kurečka Martin M Křenek Aleš A

Frontiers in molecular biosciences 20220613

AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can ...[more]

PMID: 35769910

Similar Datasets

AlphaFold-SFA: Accelerated sampling of cryptic pocket opening, protein-ligand binding and allostery by AlphaFold, slow feature analysis and metadynamics.

Project description:Sampling rare events in proteins is crucial for comprehending complex phenomena like cryptic pocket opening, where transient structural changes expose new binding sites. Understanding these rare events also sheds light on protein-ligand binding and allosteric communications, where distant site interactions influence protein function. Traditional unbiased molecular dynamics simulations often fail to sample such rare events, as the free energy barrier between metastable states is large relative to the thermal energy. This renders these events inaccessible on the timescales typically simulated by unbiased molecular dynamics, limiting our understanding of these critical processes. In this paper, we proposed a novel unsupervised learning approach termed as slow feature analysis (SFA) which aims to extract slowly varying features from high-dimensional temporal data. SFA trained on small unbiased molecular dynamics simulations launched from AlphaFold generated conformational ensembles manages to capture rare events governing cryptic pocket opening, protein-ligand binding, and allosteric communications in a kinase. Metadynamics simulations using SFA as collective variables manage to sample 'deep' cryptic pocket opening within a few hundreds of nanoseconds which was beyond the reach of microsecond long unbiased molecular dynamics simulations. SFA augmented metadynamics also managed to capture conformational plasticity of protein upon ligand binding/unbinding and provided novel insights into allosteric communication in receptor-interacting protein kinase 2 (RIPK2) which dictates protein-protein interaction. Taken together, our results show how SFA acts as a dimensionality reduction tool which bridges the gap between AlphaFold, molecular dynamics simulation and metadynamics in context of capturing rare events in biomolecules, extending the scope of structure-based drug discovery in the era of AlphaFold.

| S-EPMC11349229 | biostudies-literature

Physiological Response to Cycling With Variable Versus Constant Power Output.

Project description:Introduction: Variable power output (VP) is one of the main characteristics of a road cycling mass-start. Tolerating VP during outdoor road cycling highly influences performance. There is a lack of continuous and comprehensive measurements during this power condition. Accordingly, the aim of the present study was to investigate physiological response to VP vs. constant power output (CP) as well as the perceived exertion of these two power conditions, and to investigate if variations in power output which span above lactate threshold (LT), differ from variations below LT. Methods: 15 elite competitive cyclists completed three test days, including 1 day of baseline testing and 2 days of main testing, consisting of four bouts of 28 min at two different intensities, "low" at 70% of LT and "high" at 95% of LT, with VP and CP. VP was performed with a 15% fluctuation of the average power output every second minute. Maximal oxygen uptake (VO2), respiratory exchange ratio (RER), heart rate (HR), blood lactate (LA), rating of perceived exertion (RPE), cadence (RPM) and power output (W) were measured. Results: At both low and high intensity, the VP condition induced a significantly higher VO2, HR and LA than the CP condition. Whole-bout RPE was similar between power conditions at high intensity. Additionally, at the high intensity, cycling with VP led to a greater increase in LA and lesser increase in RPE compared to cycling with CP. Discussion: The results of this study show that, despite considerable differences in the demand during the VP and CP bouts, there are minor differences in the perceptual and physiological response directly following these two power conditions in a cohort of elite competitive cyclists. A practical implication of these findings is that training with VP seems to be a viable alternative to training with CP, at least at high intensity.

| S-EPMC7481374 | biostudies-literature

A variable refractory period increases collective performance in noisy environments.

Project description:Synchronized oscillations are found in all living systems, from cellsto ecosystems and on varying time scales. A generic principlebehind the production of oscillations involves a delay in theresponse of one entity to stimulations from the others in the sys-tem. Communication among entities is required for the emergenceof synchronization, but its efficacy can be impaired by surroundingnoise. In the social spiderAnelosimus eximius, individuals coordi-nate their activity to catch large prey that are otherwise inaccessi-ble to solitary hunters. When hunting in groups, dozens of spidersmove rhythmically toward their prey by synchronizing movingand stopping phases. We proposed a mechanistic model imple-menting individual behavioral rules, all derived fromfield experi-ments, to elucidate the underlying principles of synchronization.We showed that the emergence of oscillations in spiders involvesa refractory state, the duration of which depends on the relativeintensity of prey versus conspecific signals. Thisflexible behaviorallows individuals to rapidly adapt to variations in their vibrationallandscapes. Exploring the model reveals that the benefits of syn-chronization resulting from improved accuracy in prey detectionand reduced latency to capture prey more than offset the cost ofthe delay associated with immobility phases. Overall, our studyshows that a refractory period whose duration is variable anddependent on information accessible to all entities in the systemcontributes to the emergence of self-organized oscillations innoisy environments. Ourfindings may inspire the design of artifi-cial systems requiring fast andflexible synchronization betweentheir components.

| S-EPMC8944924 | biostudies-literature

Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering.

Project description:The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated in a Hamiltonian replica-exchange scheme. The ladder of replicas is built with different strengths of the bias potential exploiting the tunability of well-tempered metadynamics. Using this method, free-energy barriers of individual collective variables are significantly reduced compared with simple force-field scaling. The introduced methodology is flexible and allows adaptive bias potentials to be self-consistently constructed for a large number of simple collective variables, such as distances and dihedral angles. The method is tested on alanine dipeptide and applied to the difficult problem of conformational sampling in a tetranucleotide.

| S-EPMC4364913 | biostudies-literature

Output speed control for hydro-mechanical continuously variable transmission of tractor.

Project description:In order to improve the speed stability of tractors equipped with hydro-mechanical continuously variable transmission (HMCVT) during field operations, the speed regulation characteristics, torque characteristics, and power diversion characteristics of HMCVT are analyzed. And a comprehensive control strategy for HMCVT output speed is proposed by analyzing the factors affecting the fluctuation of output speed, which combines engine speed control and displacement ratio control of hydraulic speed control system. This strategy adopts fuzzy control to regulate the engine speed to maintain the stability of engine operation. Moreover, adopting feed forward compensation control for speed compensation enhances the anti-interference ability of the hydraulic speed control system. And combined with model predictive control, adjust the swash plate swing angle of the variable pump to reduce the fluctuation of HMCVT output speed. By establishing joint simulation model, the effectiveness of the control strategy was verified using tractor acceleration mode and step load disturbance mode. The results show that the strategy can reduce the fluctuation rate of engine speed and impact degree, shorten the adjustment time, and improve the stability of tractor operating speed.

| S-EPMC11421825 | biostudies-literature

Collective variable approaches for single molecule flexible fitting and enhanced sampling.

Project description: Not available

| S-EPMC3983124 | biostudies-literature

Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold.

Project description:With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., in drug design. While trusted experimental methods, such as X-ray crystallography, NMR spectroscopy, and electron microscopy, are preferred structure analysis techniques, in silico approaches are also being widely used. Two computational methods, which are on opposite ends of the spectrum with respect to their modus operandi, i.e., homology modeling and AlphaFold, have been established to provide high-quality structures. Here, a comparative study of the quality of structures either predicted by homology modeling or by AlphaFold is presented based on the characteristics determined by experimental studies using structure validation servers to fulfill the purpose. Although AlphaFold is able to predict high-quality structures, high-confidence parts are sometimes observed to be in disagreement with experimental data. On the other hand, while the structures obtained from homology modeling are successful in incorporating all aspects of the experimental structure used as a template, this method may struggle to accurately model a structure in the absence of a suitable template. In general, although both methods produce high-quality models, the criteria by which they are superior to each other are different and thus discussed in detail.

| S-EPMC10747200 | biostudies-literature

Residue Folding Degree-Relationship to Secondary Structure Categories and Use as Collective Variable.

Project description:Recently, we have shown that the residue folding degree, a network-based measure of folded content in proteins, is able to capture backbone conformational transitions related to the formation of secondary structures in molecular dynamics (MD) simulations. In this work, we focus primarily on developing a collective variable (CV) for MD based on this residue-bound parameter to be able to trace the evolution of secondary structure in segments of the protein. We show that this CV can do just that and that the related energy profiles (potentials of mean force, PMF) and transition barriers are comparable to those found by others for particular events in the folding process of the model mini protein Trp-cage. Hence, we conclude that the relative segment folding degree (the newly proposed CV) is a computationally viable option to gain insight into the formation of secondary structures in protein dynamics. We also show that this CV can be directly used as a measure of the amount of α-helical content in a selected segment.

| S-EPMC8657879 | biostudies-literature

Mapping collective variable and synergy dynamics to task outcome in a perceptual-motor skill.

Project description:In complex adaptive systems approaches to perceptual-motor control the mapping between the different categories of task dynamics: namely, task outcome, collective variable, neuromuscular synergies and individual joint configurations is a central theoretical issue, that has been primarily studied in bimanual tasks. Here we report an investigation in the roller ball task of how the task goal and multiple degrees of freedom of the arm-hand complex affords degeneracy between the respective properties of the task dynamics. The relation of the candidate collective variable, namely, the synchrony of the inner ball to outer shell motion of the roller ball and its relation to the task goal (continued increasing in ball speed), was examined as a function of the initial ball speed acting as a control parameter. Within trial analysis revealed initial search behavior for synchrony of ball and shell motion that was longer in duration with initial lower ball speed conditions. In contrast, higher initial ball speed conditions reduced the search time for and enhanced the rate of stabilization of the synchrony of inner ball and outer shell motion-features that facilitated the continued increase of ball speed and the probability of task success. Participants adopted one of three wrist-elbow neuromuscular synergies to manipulate the roller ball, the distribution of which was not influenced by either initial ball speed or task outcome. The pattern of findings over the different properties of task analysis of the roller ball provides evidence for the distinct but complementary dynamics of searching to form, stabilize and exploit a collective variable that satisfies the task goal through a small redundant set of arm-hand synergy motions.

| S-EPMC6472782 | biostudies-literature

Collective variable discovery in the age of machine learning: reality, hype and everything in between.

Project description:Understanding the kinetics and thermodynamics profile of biomolecules is necessary to understand their functional roles which has a major impact in mechanism driven drug discovery. Molecular dynamics simulation has been routinely used to understand conformational dynamics and molecular recognition in biomolecules. Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulation requires identification of a few low-dimensional variables which can describe the essential dynamics of a system without significant loss of information. In physical chemistry, these low-dimensional variables are often called collective variables. Collective variables are used to generate reduced representations of free energy surfaces and calculate transition probabilities between different metastable basins. However the choice of collective variables is not trivial for complex systems. Collective variables range from geometric criteria such as distances and dihedral angles to abstract ones such as weighted linear combinations of multiple geometric variables. The advent of machine learning algorithms led to increasing use of abstract collective variables to represent biomolecular dynamics. In this review, I will highlight several nuances of commonly used collective variables ranging from geometric to abstract ones. Further, I will put forward some cases where machine learning based collective variables were used to describe simple systems which in principle could have been described by geometric ones. Finally, I will put forward my thoughts on artificial general intelligence and how it can be used to discover and predict collective variables from spatiotemporal data generated by molecular dynamics simulations.

| S-EPMC9437778 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data