DL COMP BIOTECH

 In the ever-expanding realm of scientific research, staying updated with the latest developments and understanding the interconnectedness of various studies is a formidable challenge. This is where "Connected Papers" steps in as a valuable tool for researchers and academics. This blog will delve into the functionalities and usefulness of Connected Papers in the world of scientific exploration. The Power of Visualizing Connections Connected Papers isn't just another academic search engine; it's a bridge between the isolated islands of scientific knowledge. It harnesses the power of visualization to present academic papers in an interconnected web, allowing researchers to see at a glance how different studies relate to one another. By simply entering the title of a paper, users are presented with a network of related research. This visualization provides a holistic view of a particular field of study, making it easier to spot trends and gaps in the research landscape. ...

QSAR (Quantitative Structure-Activity Relationship) and pharmacophore modeling are two different computational techniques used in drug discovery, but they have different focuses and applications. QSAR is a technique that uses mathematical and statistical models to predict the biological activity or properties of a compound based on its chemical structure and related properties. QSAR models are generated using a set of molecular descriptors, which are numerical representations of a molecule's chemical and physical properties. QSAR models are useful for predicting the activity of compounds and for identifying potential drug candidates. Pharmacophore modeling, on the other hand, is a technique that focuses on identifying the essential structural and chemical features of a ligand (a molecule that binds to a receptor) that are responsible for its biological activity. The goal of pharmacophore modeling is to generate a three-dimensional (3D) representation of the essential features of a ...

Reaction coordinates or collective variables are quantities that are used to describe the progression of a reaction or a process of interest in a biomolecular system. Reaction coordinates or collective variables can be either geometric or dynamic, and are defined based on the specific system being studied. Geometric reaction coordinates are based on the spatial arrangement of the atoms or molecules in the system, such as the distance between two atoms, the angle between two bonds, or the dihedral angle between four atoms. Dynamic reaction coordinates, on the other hand, describe the motion or behavior of the system over time, such as the root mean square deviation (RMSD) of a protein structure, the radius of gyration, or the number of hydrogen bonds formed. By tracking reaction coordinates or collective variables, advanced MD simulations can help to identify the most probable reaction pathways, transition states, and free energy landscapes for a given process. For example, a ligand bin...

 Steered molecular dynamics (SMD) simulation is a type of molecular dynamics simulation that is used to study the response of a biomolecule to an applied force or an external perturbation. In SMD simulations, a force is applied to a subset of atoms within the biomolecule, typically using an external potential, to simulate the effect of a physical process such as protein unfolding, ligand binding or transport of ions across a membrane. The force is applied in a controlled and gradual manner, allowing the biomolecule to respond to the force and explore different conformational states. During an SMD simulation, the forces acting on the subset of atoms are continuously updated to maintain a specific pulling speed or force constant. The resulting changes in the structure and dynamics of the biomolecule can be analyzed to provide insights into its function and behavior under different conditions. SMD simulations can be used to study a wide range of biological processes, such as protein-l...

A general protocol for ligand docking using Glide: Preparation of receptor and ligand structures: The receptor structure should be prepared by adding missing atoms, assigning charges, and optimizing the geometry. The ligand structure should be prepared by generating multiple conformations and assigning charges. Generation of the receptor grid: Glide generates a grid that represents the receptor binding site. The grid is generated by defining the dimensions of the binding site, assigning van der Waals and electrostatic interaction potentials to the grid points, and scaling and translating the receptor structure to fit the grid. Ligand docking: Glide docks the ligand onto the receptor grid by generating and evaluating different ligand conformations and orientations. Glide uses a Monte Carlo algorithm to sample the conformational space and evaluate the fitness of each pose based on a scoring function. Post-docking analysis: After docking, Glide generates a list of ligand poses ranked by t...

India has made significant contributions to science and technology over the years, but it has lagged behind in Nobel Prize recognition. Some of the reasons for this could include: Lack of resources and funding : As mentioned earlier, India has limited resources and funding available for research. This can make it challenging for Indian scientists and researchers to carry out groundbreaking research, which is often a requirement for Nobel Prize recognition. Brain Drain : As talented scientists and researchers leave India to pursue better opportunities abroad, it can lead to a talent shortage in the country. This can hinder progress in research and development, and impact the chances of Indian scientists winning Nobel Prizes. Inadequate recognition and reward systems : The current reward system in India does not always value and recognize contributions to science and technology. This can discourage scientists and researchers from pursuing cutting-edge research, and make it less likely fo...

 OPLS (Optimized Potentials for Liquid Simulations) and AMBER (Assisted Model Building with Energy Refinement) force fields are widely used in molecular dynamics simulations. OPLS3 and OPLS4 are the third and fourth versions of the OPLS force field, respectively. The main difference between OPLS3 and OPLS4 is the addition of new dihedral angle parameters in OPLS4, which allow for a more accurate description of torsional profiles. OPLS4 also includes improved electrostatic parameters, including an updated partial charge model and more accurate Lennard-Jones parameters. AMBER 14SB and AMBER19SB are both versions of the AMBER force field. The main difference between AMBER 14SB and AMBER19SB is the incorporation of updated protein backbone and side-chain torsional parameters, which were derived from quantum mechanics calculations. AMBER19SB also includes updated electrostatic parameters, including the use of the AM1-BCC partial charge model. Overall, OPLS4 and AMBER19SB are more recent...

 In computational chemistry, a force field is a mathematical model used to simulate the behavior of molecules and their interactions with each other. A force field consists of a set of parameters that describe the energy and forces between atoms and molecules, and it is used to calculate the motion and properties of a molecular system. Force fields are based on classical mechanics and are often used in molecular dynamics simulations, which model the movement of molecules over time. The force field provides a mathematical description of the energy associated with the positions and orientations of the atoms in a molecule, as well as the interactions between the atoms, such as the covalent bonds, non-covalent interactions, and electrostatic interactions. There are different types of force fields that vary in their level of detail and complexity. Some force fields only consider the simplest interactions, such as Lennard-Jones potentials, while others include more sophisticated models t...

Partial charges are an essential component of force fields, which are mathematical models used in computational chemistry to simulate the behavior of molecules and their interactions with each other. Force fields rely on a set of parameters, including partial charges, to describe the energy and forces between atoms and molecules. Partial charges represent the distribution of electrons within a molecule, and they are used to calculate electrostatic interactions between atoms and molecules. These interactions are crucial for many chemical processes, such as molecular recognition, protein-ligand binding, and the stability of molecular complexes. The importance of partial charges in force fields lies in their ability to accurately describe the electrostatic interactions between atoms and molecules. Inaccurate or inappropriate partial charges can lead to erroneous predictions of molecular properties, such as the conformational energy, binding affinity, and reaction rates. Therefore, the dev...

 Molecular dynamics (MD) simulations are computer simulations that model the behavior of molecules over time, based on the principles of classical mechanics or quantum mechanics. In the context of proteins, MD simulations can be used to study the behavior and interactions of individual amino acids, as well as the overall structure and function of the protein. Classical MD simulations use classical mechanics to simulate the motion of the atoms in a protein. In a classical MD simulation, the atoms are treated as classical particles with well-defined positions and velocities, and their motion is calculated based on classical laws of motion and interatomic forces. This allows the simulation to model large protein systems over long time scales, making it a useful tool for studying the dynamics and thermodynamics of protein structures. Quantum MD simulations, on the other hand, use quantum mechanics to model the behavior of the electrons in a protein. In a quantum MD simulation, the mo...

Equilibration is an essential step before performing molecular dynamics (MD) simulations. In this step, the system is allowed to reach a stable and balanced state, or equilibrium, by adjusting its temperature, pressure, and other thermodynamic parameters to their desired values. Equilibration is necessary because MD simulations require an initial configuration of the system, and the starting configuration can significantly affect the outcome of the simulation. During equilibration, the system is typically subjected to a series of energy minimization and molecular dynamics simulations, with the parameters gradually adjusted to reach the desired equilibrium state. This process helps to remove any unphysical contacts or stresses in the system and ensures that the initial configuration is representative of the system's true equilibrium state. Equilibration is necessary because MD simulations are based on solving the equations of motion of the atoms or molecules in the system. These equ...

 How can you predict possible off targets for a given siRNA? siRNA (short interfering RNA) are small RNA molecules that can be used to silence or knockdown gene expression by binding to target messenger RNA (mRNA) and inducing its degradation. However, in addition to targeting the intended mRNA, siRNA can also bind to off-target mRNAs, leading to unintended effects. To predict possible off-targets for a given siRNA, several computational tools and databases are available, including: BLAST (Basic Local Alignment Search Tool): BLAST is a tool that allows you to compare a query sequence against a database of known sequences. By using BLAST, you can compare the siRNA sequence against a database of all known human genes and identify potential off-targets based on sequence similarity. TargetScan: TargetScan is a software tool that predicts the potential targets of miRNAs (microRNAs) and siRNAs by identifying conserved complementary sites within the 3’ untranslated region (UTR) of mRNA. T...

Nonalcoholic steatohepatitis (NASH) is a type of liver disease that is associated with obesity, diabetes, and metabolic syndrome. It is a serious condition that can lead to liver damage, cirrhosis, and liver failure. One possible connection between NASH and day-to-day phenomena or events is the prevalence of high-calorie, high-fat diets and sedentary lifestyles in modern society. Many people consume large amounts of processed and unhealthy foods and do not get enough physical activity, which can contribute to the development of NASH and other metabolic disorders. A possible analogy to illustrate this connection is the popular video game "Pac-Man." In the game, Pac-Man must navigate a maze and eat as many pellets and power-ups as possible while avoiding enemies. Similarly, in real life, our bodies must process and "eat" the food we consume while avoiding the harmful effects of excessive calorie intake and metabolic dysfunction. NASH can be thought of as a "ghost...

T he battle between Mtb ( Mycobacterium tuberculosis ) and the host can be represented as a game of chess: Mtb's opening move is to deploy its outer membrane proteins to block the host's immune response. The host responds by activating its complement system to attack Mtb's membrane, forcing it to retreat. Mtb counterattacks by secreting specialised proteins that manipulate the host's immune cells to prevent them from attacking. The host retaliates by activating its T-cells, which attack the infected cells, pushing Mtb's pieces back. Mtb tries to regroup and reposition its pieces by hiding inside the host's macrophages, but the host launches an immune response to destroy the infected cells. Mtb makes a desperate move by creating granulomas, dense clusters of infected cells, to try to survive the host's attack. The host sacrifices some of its immune cells to destroy the granulomas and finally checkmates Mtb by eradicating the infection. Overall, the battle is ...

I n recent years, personalized medicine has emerged as a promising approach to improving the diagnosis and treatment of complex diseases. By tailoring medical interventions to an individual's specific genetic, environmental, and lifestyle factors, personalized medicine has the potential to improve outcomes, reduce side effects, and lower healthcare costs. At the heart of personalized medicine lies systems biology, a multidisciplinary field that combines experimental and computational approaches to understand the complex networks of molecules, cells, and organs that underlie biological processes. The goal of systems biology is to build comprehensive models of biological systems that can be used to predict how they will respond to different perturbations, such as drug treatments or genetic mutations. These models can be used to identify new drug targets, to develop more effective and personalized treatment strategies, and to understand the underlying mechanisms of disease. One exampl...

A s we age, our cells and tissues undergo a process of degeneration that is often accompanied by chronic diseases such as cancer, Alzheimer's, and cardiovascular disease. But what if there was a way to slow down this process and keep our cells and tissues healthy for longer? That's where autophagy comes in. Autophagy is a process that occurs naturally in our bodies and is responsible for breaking down, and recycling damaged or dysfunctional cells and molecules. The word "autophagy" comes from the Greek words "auto", meaning self and "phagy", meaning eating, so autophagy literally means "self-eating." How Autophagy Works Autophagy is a complex process that involves the formation of autophagosomes, which are double-membrane vesicles that surround damaged or unwanted cellular components. The autophagosomes then fuse with lysosomes, which are organelles that contain enzymes capable of breaking down the contents of the autophagosomes. The resu...

S ystems biology is an interdisciplinary field of science that studies complex biological systems as integrated networks of interacting components using computational and experimental approaches. It aims to uncover the principles that underlie the functioning of biological systems at different levels of organization, from molecules and cells to tissues and organisms. In recent years, systems biology has become a central paradigm in the life sciences, offering a powerful framework for understanding the complexity and diversity of biological phenomena. Artificial intelligence (AI) is another field that has seen tremendous growth and development in recent years. AI involves the use of machine learning algorithms to make predictions or decisions based on large sets of data. AI has revolutionized many fields, including image and speech recognition, natural language processing, and autonomous vehicles, to name just a few. The marriage of systems biology and AI represents a potent combination...

  Provean  Website:  Reference Paper: Input: Based on sequence

beatMusic Website: http://babylone.ulb.ac.be/beatmusic Reference Paper: https://pubmed.ncbi.nlm.nih.gov/23723246/ Input: Based on structure I-Mutant Website:  Reference Paper:  Input: Based on structure SDM Website:  Reference Paper:  Input: Based on structure

The current biomedical hegemony of mass analytical "omics" domains such as genomics, proteomics, and metabolomics has enabled the rapid generation of enormously complex data sets with frequently unwieldy quantities of data points. Venn analysis is frequently used as the initial filtering step for large, complex, and interrelated data corpora. Numerous Venn diagram applications are already accessible for free to aid in the straightforward visual interpretation of biological datasets. The accessible tools for plotting Venn diagrams, on the other hand, are unevenly scattered throughout the literature. Here, we present a guided list to such tools that might assist scientists/researchers/students in visualizing their gene lists and generating biological hypotheses using integrated knowledge from biological pathway and GO databases. Table 1:List of tools and their features S. no Tool/program Number of input data sets (maximum) Platform Functional information Number of citation 1 B...