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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

146
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
146
Three-Winding Transformers01:19

Three-Winding Transformers

331
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
331
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

844
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
844
Types Of Transformers01:16

Types Of Transformers

1.1K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.1K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

229
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
229
Transformers in Distribution System01:27

Transformers in Distribution System

176
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
176

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Side-chain Packing Using SE(3)-Transformer.

Akhil Jindal1, Sergei Kotelnikov, Dzmitry Padhorny

  • 1Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Predicting protein side-chains is crucial for protein structure and design. A new 3D equivariant neural network accurately models side-chain conformations at protein-protein interfaces, addressing limitations of existing methods like AlphaFold2.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in structural biology

Background:

  • Accurate protein side-chain prediction is vital for understanding protein structure and function.
  • Existing tools like SCWRL4 offer speed and accuracy but may have limitations.
  • Recent advancements in deep learning, exemplified by AlphaFold2, show promise for complex biological modeling tasks.

Purpose of the Study:

  • To develop an advanced computational method for predicting protein side-chain conformations.
  • To specifically address the challenge of side-chain prediction within protein-protein interfaces.
  • To leverage 3D equivariant neural networks inspired by AlphaFold2's success.

Main Methods:

  • Adaptation of a 3D equivariant neural network architecture.
  • Application of the model to predict side-chain conformations.
  • Focus on modeling protein-protein interfaces, a complex structural region.

Main Results:

  • The developed neural network effectively predicts protein side-chain conformations.
  • The model shows particular promise for accurately modeling side-chains at protein-protein interfaces.
  • This approach addresses a gap not fully covered by previous state-of-the-art tools.

Conclusions:

  • 3D equivariant neural networks are a powerful tool for protein side-chain prediction.
  • The developed method offers improved accuracy for side-chain modeling at protein-protein interfaces.
  • This work advances computational approaches for protein structure prediction and design.