36 research outputs found
Deconvolution of conformational exchange from Raman spectra of aqueous RNA nucleosides
Ribonucleic acids (RNAs) are key to the central dogma of molecular biology. While Raman spectroscopy holds great potential for studying RNA conformational dynamics, current computational Raman prediction and assignment methods are limited in terms of system size and inclusion of conformational exchange. Here, a framework is presented that predicts Raman spectra using mixtures of sub-spectra corresponding to major conformers calculated using classical and ab initio molecular dynamics. Experimental optimization allowed purines and pyrimidines to be characterized as predominantly syn and anti, respectively, and ribose into exchange between equivalent south and north populations. These measurements are in excellent agreement with Raman spectroscopy of ribonucleosides, and previous experimental and computational results. This framework provides a measure of ribonucleoside solution populations and conformational exchange in RNA subunits. It complements other experimental techniques and could be extended to other molecules, such as proteins and carbohydrates, enabling biological insights and providing a new analytical tool
The prospects of quantum computing in computational molecular biology
Quantum computers can in principle solve certain problems exponentially more
quickly than their classical counterparts. We have not yet reached the advent
of useful quantum computation, but when we do, it will affect nearly all
scientific disciplines. In this review, we examine how current quantum
algorithms could revolutionize computational biology and bioinformatics. There
are potential benefits across the entire field, from the ability to process
vast amounts of information and run machine learning algorithms far more
efficiently, to algorithms for quantum simulation that are poised to improve
computational calculations in drug discovery, to quantum algorithms for
optimization that may advance fields from protein structure prediction to
network analysis. However, these exciting prospects are susceptible to "hype",
and it is also important to recognize the caveats and challenges in this new
technology. Our aim is to introduce the promise and limitations of emerging
quantum computing technologies in the areas of computational molecular biology
and bioinformatics.Comment: 23 pages, 3 figure
Otro color en mí
Curso de Especial interés: Psicología y sexualidadEl presente trabajo es una investigación descriptiva mediante la aplicación de instrumentos como lo son encuestas. Tiene como objetivo promover por medio de un espacio virtual informar a niños, niñas, jóvenes y adolescentes acerca de la menarquia y la torarquia, que les permita comprender los procesos y cambios físicos y psicológicos que se presentan en el desarrollo del ser humano durante la etapa de la pubertad. El producto consistió en el diseño y validación de una página web en la cual los niños, niñas y adolescentes encontrarán información sobre varios procesos, principalmente sobre la menarquia y la torarquia, allí también podrán enviar sus dudas y estas serán resueltas por profesionales en el tema. La muestra se encuentra conformada por jóvenes estudiantes entre los 18 a 29 años de la ciudad de Bogotá. Uno de los hallazgos más relevantes es que las participantes tuvieron su primera menstruación (menarquia) en un rango de edad entre los 8 a 16 años, siendo esto una variable muy importante para la investigación, mientras que en el caso de los hombres se evidenció que tuvieron su primera eyaculación entre los 9 a 16 años de edad.RESUMEN
1. JUSTIFICACIÓN
2. MARCO TEÓRICO
3. METODOLOGÍA
4. OBJETIVOS
5. DISEÑO
6. INSTRUMENTOS
7. PROCEDIMIENTO
8. ASPECTOS ÉTICOS
9. ESTUDIO DE MERCADEO
10. RESULTADOS
CONCLUSIONES
REFERENCIAS
ANEXOSPregradoPsicólog
Current structure predictors are not learning the physics of protein folding
Summary
Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state.
Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding.
Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/</p
Current structure predictors are not learning the physics of protein folding
Summary: Motivation
Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state.
Results
In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding.
Availability
The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/
Supplementary information
Supplementary data are available at Bioinformatics online
Investigating the potential for a limited quantum speedup on protein lattice problems
Protein folding is a central challenge in computational biology, with
important applications in molecular biology, drug discovery and catalyst
design. As a hard combinatorial optimisation problem, it has been studied as a
potential target problem for quantum annealing. Although several experimental
implementations have been discussed in the literature, the computational
scaling of these approaches has not been elucidated. In this article, we
present a numerical study of quantum annealing applied to a large number of
small peptide folding problems, aiming to infer useful insights for near-term
applications. We present two conclusions: that even naive quantum annealing,
when applied to protein lattice folding, has the potential to outperform
classical approaches, and that careful engineering of the Hamiltonians and
schedules involved can deliver notable relative improvements for this problem.
Overall, our results suggest that quantum algorithms may well offer
improvements for problems in the protein folding and structure prediction
realm.Comment: 45 pages, 18 figure
Investigating the potential for a limited quantum speedup on protein lattice problems
Protein folding, the determination of the lowest-energy configuration of a
protein, is an unsolved computational problem. If protein folding could be
solved, it would lead to significant advances in molecular biology, and
technological development in areas such as drug discovery and catalyst design.
As a hard combinatorial optimisation problem, protein folding has been studied
as a potential target problem for adiabatic quantum computing. Although several
experimental implementations have been discussed in the literature, the
computational scaling of these approaches has not been elucidated. In this
article, we present a numerical study of the (stoquastic) adiabatic quantum
algorithm applied to protein lattice folding. Using exact numerical modelling
of small systems, we find that the time-to-solution metric scales exponentially
with peptide length, even for small peptides. However, comparison with
classical heuristics for optimisation indicates a potential limited quantum
speedup. Overall, our results suggest that quantum algorithms may well offer
improvements for problems in the protein folding and structure prediction
realm