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Related Concept Videos

Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
PD Controller: Design01:26

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.

Francesco Caredda1, Lisa Gennai2, Paolo De Los Rios2,3

  • 1Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy.

Plos Computational Biology
|February 19, 2026
PubMed
Summary

FeatureDCA enhances protein sequence generation by incorporating biological data, enabling targeted design with high accuracy and structural realism. This statistical framework improves protein modeling and design by conditioning generative processes effectively.

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Area of Science:

  • Computational biology
  • Protein engineering
  • Statistical modeling

Background:

  • Direct Coupling Analysis (DCA) is a statistical method for protein sequence modeling.
  • Existing generative models may lack biological context or fine-grained control.
  • Protein design requires methods that balance generative accuracy with biological relevance.

Purpose of the Study:

  • To introduce FeatureDCA, a novel statistical framework for protein sequence modeling and generation.
  • To extend DCA by incorporating biologically meaningful conditioning for improved protein design.
  • To demonstrate FeatureDCA's ability to guide sequence generation toward specific functional or structural properties.

Main Methods:

  • FeatureDCA extends Direct Coupling Analysis (DCA) with conditioning on biological information (e.g., phylogeny, temperature, principal components).
  • An autoregressive implementation of FeatureDCA was developed for sequence generation.
  • Generated sequences were validated using structural prediction tools (AlphaFold, ESMFold) and compared against experimental data (deep mutational scanning).

Main Results:

  • FeatureDCA matches or surpasses established models in generative accuracy for higher-order sequence statistics across multiple protein families.
  • Generated sequences maintain substantial diversity and adopt biologically plausible folds consistent with wild-type targets.
  • In a case study of Response Regulators, FeatureDCA accurately reproduced class-specific architectures when conditioned on subtype-specific principal components.
  • FeatureDCA predictions showed accuracy comparable to unconditioned models for deep mutational scanning data, indicating capture of local functional constraints.

Conclusions:

  • FeatureDCA offers a flexible and transparent approach for targeted protein sequence generation.
  • The framework effectively bridges statistical fidelity, structural realism, and interpretability in protein design.
  • FeatureDCA demonstrates potential for fine-grained structural control and accurate modeling of functional constraints in protein engineering.