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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.0K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.1K
Dynamic Equilibrium02:20

Dynamic Equilibrium

62.0K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
62.0K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Data Collection I01:30

Data Collection I

7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K
Data Validation01:03

Data Validation

6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Updated: Jan 24, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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DynaBench: Datos dinámicos para el benchmark de docking

Aye Berçin Barlas1, Benoist Laurent2, Ezgi Karaca1

  • 1Izmir Biomedicine and Genome Center, Izmir, Turkey.

Journal of molecular biology
|January 22, 2026
PubMed
Resumen
Este resumen es generado por máquina.

DynaBench ofrece un nuevo benchmark para la dinámica de interacciones proteína-proteína utilizando simulaciones de dinámica molecular. Este recurso ayuda a comprender la flexibilidad interfacial y a mejorar la precisión del modelado estructural.

Palabras clave:
Dinámica molecular de átomo completoDinámica de proteínasInterfaces de proteínasInteracciones proteína-proteínadocking

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Área de la Ciencia:

  • Bioquímica
  • Biología Estructural
  • Biología Computacional

Sus antecedentes:

  • Las interacciones proteína-proteína son cruciales para las funciones celulares como el transporte y la señalización.
  • Las herramientas actuales de modelado estructural (por ejemplo, AlphaFold) proporcionan representaciones estáticas, descuidando la flexibilidad crítica de la interfaz.
  • Comprender la dinámica de las interfaces de proteínas es esencial para obtener información funcional precisa.

Objetivo del estudio:

  • Introducir DynaBench, un benchmark integral para la dinámica de interfaces proteína-proteína.
  • Proporcionar un conjunto de datos a gran escala de simulaciones de dinámica molecular (MD) para complejos de proteínas.
  • Facilitar avances en el modelado computacional y el análisis de ensamblajes de proteínas.

Principales métodos:

  • Se realizaron simulaciones extensivas de MD en más de 200 complejos de proteína-proteína del Docking Benchmark 5.5.
  • Se generaron tres réplicas de trayectoria de 100 ns de duración para cada complejo.
  • Todos los datos de simulación se pusieron a disposición del público a través de la plataforma MDposit dentro del Molecular Dynamics Data Bank (MDDB).

Principales resultados:

  • Se generó un conjunto de datos sustancial de dinámica de complejos de proteínas, capturando la flexibilidad interfacial.
  • Se estableció un recurso valioso para entrenar modelos de aprendizaje automático para la predicción de estructuras de proteínas.
  • Se permitió la exploración de nuevas métricas de precisión para evaluar modelos de complejos de proteínas.

Conclusiones:

  • DynaBench aborda la limitación de los modelos estáticos al proporcionar datos cruciales de dinámica de interfaz.
  • El benchmark sirve como un recurso clave para la biología estructural computacional y el diseño de fármacos.
  • Los datos de acceso público promueven una mayor investigación en el modelado de complejos de proteínas y el análisis funcional.