Structural Alignment Module

The Structural Alignment Module is specifically developed to manage and execute the structural alignment of protein sequences. This sophisticated system is designed to handle various alignment algorithms, facilitating the comparison and analysis of protein 3D structures. It integrates with Python’s multiprocessing framework and SQLAlchemy for efficient data handling and storage, making it a powerful tool for computational biology and bioinformatics research. Class Overview ————–

The StructuralAlignmentManager class is the core of this module, tailored for aligning protein structures within clusters identified by the CDHit module. It uses diverse structural alignment algorithms.

class protein_metamorphisms_is.operations.structural_alignment.StructuralAlignmentManager(conf)

Bases: OperatorBase

Manages the structural alignment process of proteins using various alignment algorithms.

This class extends OperatorBase to implement a task management system for structural alignment, allowing the execution of different alignment tasks that are configurable through dynamic modules.

conf

Configuration of the instance, including database connections and operational settings.

Type:

dict

check_empty_queue()

Determines if the alignment queue is empty, indicating no pending or error tasks awaiting processing.

Evaluates the alignment queue to assess the presence of tasks in ‘pending’ or ‘error’ status that have not yet exceeded the retry limit. This is crucial for deciding whether to fetch new tasks or proceed with retrying error tasks. The method specifically looks for tasks that are either awaiting initial processing (state 0) or have encountered errors but are eligible for retry (state 3), based on a retry count that is below a predefined threshold set in the configuration.

Returns:

True if the queue is empty, indicating there are no tasks in ‘pending’ or ‘error’ status

that require processing. False otherwise, suggesting that there are tasks that need attention.

Return type:

bool

execute_aligns(queue_items)

Executes alignment tasks for a batch of queue items using multiprocessing.

This method leverages Python’s multiprocessing capabilities to perform structural alignments in parallel, according to the number of workers specified in the configuration (max_workers). Each alignment task is executed asynchronously with a timeout limit (task_timeout), also specified in the configuration. The method handles both successful completions and timeouts by logging the outcomes and storing the results or errors.

The alignment tasks are dynamically determined based on the alignment type associated with each queue item, allowing for flexibility in the alignment process. Results from completed tasks are collected and later inserted into the database.

Parameters:

queue_items (list) – A list of queue items to be aligned. Each item contains necessary information for performing the alignment, including the type of alignment to execute.

Notes

• The method logs the start and completion of the alignment process, including any errors encountered.

• Upon completion or timeout, results are passed to insert_results for database insertion.

fetch_queue_items()

Retrieves a batch of alignment tasks from the queue, prioritizing those in ‘pending’ state.

This method performs a complex query to the database to fetch a specified number of alignment tasks, up to the ‘batch_size’ limit defined in the configuration. It includes tasks that are either pending execution (state 0) or have previously encountered errors and are eligible for retry (state 3), provided they have not exceeded the maximum retry count also specified in the configuration.

The method constructs a subquery to identify the representative structure for each cluster involved in the alignment tasks, facilitating a direct comparison between target structures and their respective representatives. It then joins this subquery with the main queue items query to ensure each task fetched includes comprehensive information about the target structure and its representative counterpart.

Returns:

A list of SQLAlchemy model instances, each representing a queue item. These instances include

detailed information about the task, such as the alignment type, cluster ID, PDB IDs, chain identifiers, and model numbers for both the target structure and its representative.

Return type:

list

fetch_tasks_info()

Fetches and prepares alignment task modules based on the configuration.

This method dynamically imports alignment task modules specified in the configuration and stores references to these modules in a dictionary for later use in the alignment process.

get_update_queue()

Updates the alignment queue with new tasks and manages stale entries.

This method loads clusters from the database, updates the queue with new clusters not yet queued for alignment, and resets the state of stale entries in the queue.

insert_results(results)

Inserts the outcomes of structural alignment tasks into the database and updates the status of queue items.

After structural alignment tasks are completed, this method is responsible for processing the results, which may include successful alignment data or error messages for tasks that failed. Depending on the outcome, it updates the database with the alignment results for successful tasks or logs and records errors for tasks that encountered issues. Additionally, it updates the status of each task in the alignment queue to reflect its current state, whether completed successfully, failed with an error, or pending retry based on the retry policy defined in the configuration.

Parameters:

results (list) – A list containing the results of the alignment tasks. Each element in the list is a tuple where the first element is the queue entry ID, and the second element is a dictionary with either the alignment results (e.g., RMS values) for successful tasks or an error message for tasks that failed.

Process:

• Iterates through the list of results, processing each based on its content (success or failure).

• For successful tasks, stores alignment results (e.g., RMS values) in the database.

• For tasks that failed, logs the error and updates the task’s status in the queue to ‘error’ (state 3), while also incrementing the retry count for possible future attempts.

• Updates the task’s status in the queue to ‘completed’ (state 2) for successful alignments.

• Commits all changes to the database once all results are processed.

Note

The method ensures that the alignment queue is accurately updated to reflect the outcome of each task, facilitating efficient management of pending, in-process, and completed tasks.

start()

Begin the structural alignment process.

This method manages the workflow of the alignment process, including loading clusters, executing alignments, and handling any exceptions encountered during the process. Progress and errors are logged appropriately.

Key Features

• Cluster-Based Structural Alignment: Aligns protein structures within clusters generated by CDHit.

• Database Integration: Efficiently manages protein structure data in SQL databases.

• Concurrent Processing: Utilizes multi-threading for aligning multiple structures in parallel.

• Robust Error Handling: Implements comprehensive error handling for reliable alignment operations.

SQL/ORM Entities

The module interacts with SQL/ORM entities relevant to protein clustering and structural data:

• PDBChains: Manages information about Protein Data Bank chains.

• Cluster: Stores data for clusters created by CDHit, crucial for alignment.

• CEAlignResults: Records the outcomes of the CE alignment process.

These entities facilitate the organization and retrieval of protein structure data and alignment results within and across clusters.

Configuration

StructuralAlignmentManager requires specific configuration parameters for execution:

# Structural Alignment
structural_alignment: [Methods to be used]
  types:
    - 1 [CE-Align]
    - 2 [US-Align]
    - 3 [FATCAT]

  retry_timeout: [Time to requeue]
  retry_count: [Number of attempts]
  batch_size: [Number of executions per queue iteration]
  task_timeout: [Maximum duration of the operation before being canceled]

These settings should be adjusted based on your structural alignment requirements.

To incorporate the information about the alignment task functions located in the specified files into your documentation, you can add a section titled “Available Tasks” that describes each of the alignment algorithms implemented by these functions. Here’s how you could insert them into the documentation:

Available Tasks

• Combinatorial Extension Alignment Task
  • align_task()
• FATCAT Alignment Task
  • align_task()
• Universal Structural Alignment Task
  • align_task()

Usage

To use StructuralAlignmentManager, initialize the class with the configuration and begin the alignment process:

from protein_metamorphisms_is.operations.structural_alignment import StructuralAlignmentManager
# Start structural alignment
StructuralAlignmentManager(conf).start()

This starts the structural alignment process, handling aspects from data loading to execution and storage, focusing on protein structures within the clusters generated by CDHit.