The Two-Step Clustering Approach for Metastable States Learning.

Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein-protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.

Current Challenges and Limitations in the Studies of Intrinsically Disordered Proteins in Neurodegenerative Diseases by Computer Simulations.

Experiments face challenges in the analysis of intrinsically disordered proteins in solution due to fast conformational changes and enhanced aggregation propensity. Computational studies complement experiments, being widely used in the analyses of intrinsically disordered proteins, especially those positioned at the centers of neurodegenerative diseases. However, recent investigations - including our own - revealed that computer simulations face significant challenges and limitations themselves. In this review, we introduced and discussed some of the scientific challenges and limitations of computational studies conducted on intrinsically disordered proteins. We also outlined the importance of future developments in the areas of computational chemistry and computational physics that would be needed for generating more accurate data for intrinsically disordered proteins from computer simulations. Additional theoretical strategies that can be developed are discussed herein.

Predicting Density of Amorphous Solid Materials Using Molecular Dynamics Simulation.

The true density of an amorphous solid is an important parameter for studying and modeling materials behavior. Experimental measurements of density using helium pycnometry are standard but may be prevented if the material is prone to rapid recrystallization, or preparation of gram quantities of reproducible pure component amorphous materials proves impossible. The density of an amorphous solid can be approximated by assuming it to be 95% of its respective crystallographic density; however, this can be inaccurate or impossible if the crystal structure is unknown. Molecular dynamic simulations were used to predict the density of 20 amorphous solid materials. The calculated density values for 10 amorphous solids were compared with densities that were experimentally determined using helium pycnometry. In these cases, the amorphous densities calculated using molecular dynamics had an average percent error of - 0.7% relative to the measured values, with a maximum error of - 3.48%. In contrast, comparisons of amorphous density approximated from crystallographic structures with pycnometrically measured values resulted in an average percent error of + 3.7%, with a maximum error of + 9.42%. These data suggest that the density of an amorphous solid can be accurately predicted using molecular dynamic simulations and allowed reliable calculation of density for the remaining 10 materials for which pycnometry could not be done.

Molecular dynamics in cells: A neutron view.

Experiments to characterize intracellular molecular dynamics in vivo are discussed following a description of the incoherent neutron scattering method. Work reviewed includes water diffusion in bacteria, archaea, red blood cells, brain cells and cancer cells, and the role of proteome molecular dynamics in adaptation to physiological temperature and pressure, and in response to low salt stress in an extremophile. A brief discussion of the potential links between neutron scattering results and MD simulations on in-cell dynamics concludes the review.

Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge.

The in silico prediction of partition coefficients is an important task in computer-aided drug discovery. In particular the octanol-water partition coefficient is used as surrogate for lipophilicity. Various computational approaches have been proposed, ranging from simple group-contribution techniques based on the 2D topology of a molecule to rigorous methods based molecular dynamics (MD) or quantum chemistry. In order to balance accuracy and computational cost, we recently developed the MD fingerprints (MDFPs), where the information in MD simulations is encoded in a floating-point vector, which can be used as input for machine learning (ML). The MDFP-ML approach was shown to perform similarly to rigorous methods while being substantially more efficient. Here, we present the application of MDFP-ML for the prediction of octanol-water partition coefficients in the SAMPL6 blind challenge. The underlying computational pipeline is made freely available in form of the MDFPtools package.

Computational microscopy: Revealing molecular mechanisms in plants using molecular dynamics simulations.

plantcell;31/12/tpc.119.tt1219/FIG1F1fig1Structural biology has provided valuable insights and high-resolution views of the biophysical processes in plants, such as photosynthesis, hormone signaling, nutrient transport, and toxin efflux. However, structural biology only provides a few "snapshots" of protein structure, whereas in vivo, protein function involves complex dynamical processes such as ligand binding and conformational changes that structures alone are unable to capture in full detail. Here, we present all-atom molecular dynamics (MD) simulations as a "computational microscope" that can be used to capture detailed structural and dynamical information about the molecular machinery in plants and gain high-resolution insights into plant growth and function. In addition to the background information provided here, we have prepared a set of tutorials that allow students to run and explore MD simulations of plant proteins.(Posted December 10, 2019)Click HERE to access Teaching Tools ComponentsRECOMMENDED CITATION STYLE:Feng, J., Chen, J., Selvam, B., and Shukla, D. (December 10, 2019). Computational microscopy: Revealing molecular mechanisms in plants using molecular dynamics simulations. Teaching Tools in Plant Biology: Lecture Notes. The Plant Cell (online), doi/ /10.1105/tpc.tt1219.

Bringing Molecular Dynamics Simulation Data into View.

Molecular dynamics (MD) simulations monitor time-resolved motions of macromolecules. While visualization of MD trajectories allows an instant and intuitive understanding of dynamics and function, so far mainly static representations are provided in the published literature. Recent advances in browser technology may allow for the sharing of trajectories through interactive visualization on the web. We believe that providing intuitive and interactive visualization, along with related protocols and analysis data, promotes understanding, reliability, and reusability of MD simulations. Existing barriers for sharing MD simulations are discussed and emerging solutions are highlighted. We predict that interactive visualization of MD trajectories will quickly be adopted by researchers, research consortiums, journals, and funding agencies to gather and distribute results from MD simulations via the web.

Computational Approaches to Studying Voltage-Gated Ion Channel Modulation by General Anesthetics.

Voltage-gated ion channels (VGICs) are responsible for the propagation of electrical signals in excitable cells. Small-molecule modulation of VGICs affects transmission of action potentials in neurons and thus can modulate the activity of the central nervous system. For this reason, VGICs are considered key players in the medically induced state of general anesthesia. Consistently, VGICs have been shown to respond to several general anesthetics. However, in spite of extensive electrophysiological characterizations, modulation of VGICs by anesthetics is still only partially understood. Among the challenging aspects are the presence of multiple binding sites and the observation of paradoxical effects, i.e., evidence, for the same channel, of inhibition and potentiation. In this context, molecular simulations emerged in the recent past as the tool of choice to complement electrophysiology studies with a microscopic picture of binding and allosteric regulation. In this chapter, we describe the most effective computational techniques to study VGIC modulation by general anesthetics. We start by reviewing the VGIC conduction cycle, the corresponding set of channel conformations, and the approaches used to model them. We then review the most successful strategies to identify binding sites and estimate binding affinities.

Identifying Functional Hotspot Residues for Biased Ligand Design in G-Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) mediate multiple signaling pathways in the cell, depending on the agonist that activates the receptor and multiple cellular factors. Agonists that show higher potency to specific signaling pathways over others are known as "biased agonists" and have been shown to have better therapeutic index. Although biased agonists are desirable, their design poses several challenges to date. The number of assays to identify biased agonists seems expensive and tedious. Therefore, computational methods that can reliably calculate the possible bias of various ligands ahead of experiments and provide guidance, will be both cost and time effective. In this work, using the mechanism of allosteric communication from the extracellular region to the intracellular transducer protein coupling region in GPCRs, we have developed a computational method to calculate ligand bias ahead of experiments. We have validated the method for several ß-arrestin-biased agonists in ß2-adrenergic receptor (ß2AR), serotonin receptors 5-HT1B and 5-HT2B and for G-protein-biased agonists in the κ-opioid receptor. Using this computational method, we also performed a blind prediction followed by experimental testing and showed that the agonist carmoterol is ß-arrestin-biased in ß2AR. Additionally, we have identified amino acid residues in the biased agonist binding site in both ß2AR and κ-opioid receptors that are involved in potentiating the ligand bias. We call these residues functional hotspots, and they can be used to derive pharmacophores to design biased agonists in GPCRs.

Emergence of hydrogen bonds from molecular dynamics simulation of substituted N-phenylthiourea-catechol oxidase complex.

A series of N-phenylthiourea derivatives was built starting from the X-ray structure in the molecular mechanics framework and the interaction profile in the complex with the catechol oxidase was traced using molecular dynamics simulation. The results showed that the geometry and interactions between ligand and receptor were highly related to the position of the substituted side chains of phenyl moiety. At the end of molecular dynamics run, a concentrated multicenter hydrogen bond was created between the substituted ligand and receptor. The conformation of the ligand itself were also restricted in the receptor pocket. Furthermore, the simulation time of 50 ns were found to be long enough to explore the relevant conformational space and the stationary behavior of the molecular dynamic could be observed.

Posibilidad de aplicación de la simulación computacional de tejido óseo en niños con torsión tibial / Possible application de la modélisation assistée par ordinateur des tissus osseux chez des enfants atteints de torsion tibiale / Possibility of application of computational simulation of bone tissue in children with tibial torsion

Introducción: con el desarrollo de la informática surgen nuevos caminos a soluciones de problemas en la práctica clínica. La modelación de tejidos desempeña un papel importante en el desarrollo de la medicina; la experimentación en pacientes vivos dificulta la obtención de resultados, de ahí la necesidad de buscar alternativas para mejorar la calidad del servicio de salud. Objetivo: valorar la importancia de la modelación computacional de tejidos biológicos en niños con torsión tibial. Métodos: se entrevistaron 44 especialistas entre doctores, técnicos en imágenes médicas, ingenieros mecánicos e ingenieros cibernéticos. Fue empleada una encuesta no estructurada sin guion previo. Resultados: se aplicaron valores empíricos de cargas para corregir deformidades como la torsión tibial, el 81 por ciento de los encuestados conocen acerca las ventajas de las simulaciones computacionales aplicadas a la salud, el 17 por ciento opina que faltan recursos informáticos en los hospitales para emplear estas técnicas, el 2 por ciento cree que se debe capacitar a los doctores en el empleo de estas herramientas para apoyar la toma de decisiones y el diagnóstico clínico. Conclusiones: la encuesta proporcionó datos conclusivos sobre la posibilidad e interés de la aplicación de los modelos computacionales en el diagnóstico, pronóstico y seguimiento de enfermedades ortopédicas(AU)

Introduction: the development of information brings new paths to solve problems in the clinical practice. Tissue modeling plays an important role in the development of medicine, experimentation on living patients makes it difficult to some extent the results, hence the need to seek alternatives to improve the quality of health service. Objective: assess the importance of computational modeling of biological tissues in pediatric orthopedics, specifically in children with tibial torsion. Methods: forty-four specialists were interviewed including physicians, technicians in medical imaging, mechanical engineers, and cyber engineers. It an unscripted survey unstructured was used. Results: empirical values of loads are applied to correct deformities such as tibial torsion; 81 percent of respondents know about the advantages of computer simulations for health, 17 percent think that missing computer resources in hospitals to employ these techniques, 2 percent believes that doctors should be trained in the use of these tools to support decision-making and clinical diagnosis. Conclusions: the survey provided conclusive data on the ability and interest of the application of computational models in the diagnosis, prognosis, and monitoring of orthopedic diseases(AU)

Introduction: à fur et à mesure que l'informatique se développe, de nouvelles solutions aux problèmes de la pratique clinique apparaissent. La modélisation de tissus joue un rôle essentiel dans le développement de la médecine; l'expérimentation sur des patients vivants empêche l'obtention de résultats, il est pourtant nécessaire de chercher d'autres alternatives afin d'améliorer la qualité du service sanitaire. Objectif: le but de cette étude est d'évaluer l'importance de la modélisation assistée par ordinateur des tissus biologiques chez des enfants atteints de torsion tibiale. Méthodes: Quarante et quatre spécialistes, tels que médecins, techniciens en imagerie médicale, ingénieurs mécaniques et ingénieurs cybernétiques, ont été enquêtés. L'enquête utilisée n'avait aucune structure, aucun plan à suivre. Résultats: Des valeurs empiriques de charge ont été utilisées pour corriger des déformations, telles que la torsion tibiale; la majorité des enquêtés (81 pourcent) connaissent bien les bénéfices de la modélisation assistée par ordinateur appliqués à la santé ; la moitié (17 pourcent) considère qu'il y a un déficit de ressources informatiques dans les hôpitaux pour employer cette technique, tandis que la minorité (2 pourcent) croie qu'il faut que les médecins acquissent les habiletés nécessaires pour utiliser cet outil dans la prise de décisions et le diagnostic clinique. Conclusions: L'enquête a fourni des données incontestables sur la possibilité et l'intérêt à mettre en application la modélisation assistée par ordinateur dans le diagnostic, le pronostic et le suivi d'affections orthopédiques (AU)

Molecular Dynamics: New Frontier in Personalized Medicine.

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

Can all heritable biology really be reduced to a single dimension?

A long-held presupposition in the field of bioinformatics holds that genetic, and now even epigenetic 'information' can be abstracted from the physicochemical details of the macromolecular polymers in which it resides. It is perhaps rather ironic that this basic conjecture originated upon the first observations of DNA structure itself. This static model of DNA led very quickly to the conclusion that only the nucleobase sequence itself is rich enough in molecular complexity to replicate a complex biology. This idea has been pervasive throughout genomic science, higher education and popular culture ever since; to the point that most of us would accept it unquestioningly as fact. What is more alarming is that this conjecture is driving a significant portion of the technological development in modern genomics towards methods strongly rooted in DNA sequencing, thereby reducing a dynamic multi-dimensional biology into single-dimensional forms of data. Evidence countering this central tenet of bioinformatics has been quietly mounting over many decades, prompting some to propose that the genome must be studied from the perspective of its molecular reality, rather than as a body of information to be represented symbolically. Here, we explore the epistemological boundary between bioinformatics and molecular biology, and warn against an 'overtly' bioinformatic perspective. We review a selection of new bioinformatic methods that move beyond sequence-based approaches to include consideration of databased three dimensional structures. However, we also note that these hybrid methods still ignore the most important element of gene function when attempting to improve outcomes; the fourth dimension of molecular dynamics over time.

Monte Carlo simulation of the basic features of the GE Millennium MG single photon emission computed tomography gamma camera / Simulación de Monte Carlo de los rasgos básicos de la cámara gamma SPECT GE Millennium MG

OBJECTIVE: To describe and validate the simulation of the basic features of GE Millennium MG gamma camera using the GATE Monte Carlo platform. MATERIAL AND METHODS: Crystal size and thickness, parallel-hole collimation and a realistic energy acquisition window were simulated in the GATE platform. GATE results were compared to experimental data in the following imaging conditions: a point source of 99mTc at different positions during static imaging and tomographic acquisitions using two different energy windows. The accuracy between the events expected and detected by simulation was obtained with the Mann-Whitney-Wilcoxon test. Comparisons were made regarding the measurement of sensitivity and spatial resolution, static and tomographic. Simulated and experimental spatial resolutions for tomographic data were compared with the Kruskal-Wallis test to assess simulation accuracy for this parameter. RESULTS: There was good agreement between simulated and experimental data. The number of decays expected when compared with the number of decays registered, showed small deviation (<=0.007%). The sensitivity comparisons between static acquisitions for different distances from source to collimator (1, 5, 10, 20, 30 cm) with energy windows of 126-154 keV and 130-158 keV showed differences of 4.4%, 5.5%, 4.2%, 5.5%, 4.5% and 5.4%, 6.3%, 6.3%, 5.8%, 5.3%, respectively. For the tomographic acquisitions, the mean differences were 7.5% and 9.8% for the energy window 126-154 keV and 130-158 keV. Comparison of simulated and experimental spatial resolutions for tomographic data showed no statistically significant differences with 95% confidence interval. CONCLUSIONS: Adequate simulation of the system basic features using GATE Monte Carlo simulation platform was achieved and validated

Objetivo. Describir y validar la simulación de características básicas de la cámara gamma GE Millennium MG utilizando la plataforma GATE Monte Carlo. MATERIAL Y MÉTODOS: El tamaño y espesor del cristal, la colimación de agujeros paralelos y una ventana de adquisición de energía realista se simularon en la plataforma GATE. Los resultados GATE se compararon con los datos experimentales en las siguientes condiciones de formación de imágenes: fuente puntual 99mTc en diferentes posiciones durante la adquisición de imágenes estáticas y tomográficas utilizando 2 diferentes ventanas de energía. La precisión entre los eventos esperados y detectados por simulación se realizó utilizando la prueba de Mann-Whitney-Wilcoxon. Las comparaciones se hicieron con respecto a las medidas de los parámetros sensibilidad y resolución espacial, estáticas y tomográficas. Las resoluciones espaciales simulada y experimental de los datos tomográficos se compararon con la prueba de Kruskal-Wallis. RESULTADOS: Hubo buena concordancia entre los datos simulados y experimentales. El número de decaimientos esperado en comparación con los registrados, ha revelado una pequeña desviación (<=0,007%). Las comparaciones de sensibilidad entre las adquisiciones estáticas, para diferentes distancias desde la fuente al colimador (1, 5, 10, 20, 30 cm) con ventanas de energía de 126-154 keV y 130-158 keV, mostraron diferencias de 4,4; 5,5; 4,2; 5,5; 4,5 y 5,4; 6,3; 6,3; 5,8, y 5,3%, respectivamente. Las comparaciones entre sensibilidad tomográfica fueron 7,5 y 9,8% para la ventana de energía 126-154 keV y 130-158 eV. La comparación de resoluciones espaciales simuladas y experimentales para los datos tomográficos no ha mostrado diferencias estadísticamente significativas con un intervalo de confianza del 95%. CONCLUSIONES: Se ha conseguido efectuar y validar una simulación Monte Carlo con la plataforma GATE de características básicas de funcionamiento de una cámara gamma GE Millennium MG

Understanding protein dynamics using conformational ensembles.

Conformational ensembles are powerful tools to represent the range of conformations that can be sampled by proteins. They can be generated by using purely theoretical methods or, as is most often the case, by fitting ensembles of conformations to experimental data that report on the amplitude of protein dynamics. Conformational ensembles have been useful instruments to study fundamental properties of proteins such as the mechanism of molecular recognition, the early stages of protein folding and the mechanism by which structural information propagates through the structures of globular proteins structures via correlated backbone motions. In this chapter I will review the various approaches that have been put forward in the literature to generate conformation ensembles for proteins and present a selection of examples of how such representations of the structural heterogeneity of proteins have been used to explore the fundamental properties of these macromolecules. Finally, I will look ahead at likely future developments in this area, which is important for structural and chemical biology as well as for biophysics.

Imaging Ca2+ nanosparks in heart with a new targeted biosensor.

RATIONALE: In cardiac dyads, junctional Ca2+ directly controls the gating of the ryanodine receptors (RyRs), and is itself dominated by RyR-mediated Ca2+ release from the sarcoplasmic reticulum. Existing probes do not report such local Ca2+ signals because of probe diffusion, so a junction-targeted Ca2+ sensor should reveal new information on cardiac excitation-contraction coupling and its modification in disease states. OBJECTIVE: To investigate Ca2+ signaling in the nanoscopic space of cardiac dyads by targeting a new sensitive Ca2+ biosensor (GCaMP6f) to the junctional space. METHODS AND RESULTS: By fusing GCaMP6f to the N terminus of triadin 1 or junctin, GCaMP6f-triadin 1/junctin was targeted to dyadic junctions, where it colocalized with t-tubules and RyRs after adenovirus-mediated gene transfer. This membrane protein-tagged biosensor displayed ≈4× faster kinetics than native GCaMP6f. Confocal imaging revealed junctional Ca2+ transients (Ca2+ nanosparks) that were ≈50× smaller in volume than conventional Ca2+ sparks (measured with diffusible indicators). The presence of the biosensor did not disrupt normal Ca2+ signaling. Because no indicator diffusion occurred, the amplitude and timing of release measurements were improved, despite the small recording volume. We could also visualize coactivation of subclusters of RyRs within a single junctional region, as well as quarky Ca2+ release events. CONCLUSIONS: This new, targeted biosensor allows selective visualization and measurement of nanodomain Ca2+ dynamics in intact cells and can be used to give mechanistic insights into dyad RyR operation in health and in disease states such as when RyRs become orphaned.

Overview of computer modeling of cellulose.

Although it has a deceptively simple primary structure, the collective organization of bulk cellulose, particularly as it exists in cellulose fibers in the cell walls of living plants and other organisms, is quite diverse and complex. While some experimental techniques, such as vibrational spectroscopy and diffraction from partially crystalline samples, are able to provide insights into the organization of bulk cellulose, its intrinsic complexity has left many questions still unanswered. For this reason, additional probes of cellulose structure would be highly desirable. With the continuing advances in computer power through massive parallelization, and the steady progress in computer codes and force fields for modeling carbohydrate systems, molecular mechanics simulations have become an attractive means of studying cellulosic systems at the atomic and molecular level. The coming decade will almost certainly see remarkable advances in the understanding of cellulose using such simulations.

The errors of our ways: taking account of error in computer-aided drug design to build confidence intervals for our next 25 years.

The future of the advancement as well as the reputation of computer-aided drug design will be guided by a more thorough understanding of the domain of applicability of our methods and the errors and confidence intervals of their results. The implications of error in current force fields applied to drug design are given are given as an example. Even as our science advances and our hardware become increasingly more capable, our software will be perhaps the most important aspect in this realization. Some recommendations for the future are provided. Education of users is essential for proper use and interpretation of computational results in the future.