The analysis of functional domains is fundamental to understanding the structure of proteins. It is essential to identify and characterise specific regions of proteins in order to elucidate their functionality, mode of evolution and important interactions.
PFAM - Protein families
Pfam is a tool used to know protein families information, developed to help researchers to identify and analyze domain and protein families based on sequence.
Input
Access the site and search for families. You can use a search bar to search and type family name or interest sequence (FASTA format), and press enter. You can explore family protein by categories and using a list of families.
Algorithm
Pfam is based around hidden Markov model (HMM) searches, as provided by the HMMER3 package. In HMMER3, like BLAST, E-values (expectation values) are calculated. The E-value is the number of hits that would be expected to have a score equal to or better than this value by chance alone. A good E-value is much less than 1. A value of 1 is what would be expected just by chance. In principle, all you need to decide on the significance of a match is the E-value. HMMER3 calculates two kinds of scores, the first for the sequence as a whole and the second for the domain(s) on that sequence.
Results
Pfam results provide a detailed and structured view of protein domains, helping to understand the functions and structures of proteins and to explore their biological applications.
This program is a powerful tool for analyzing and interpreting data on proteins, providing a detailed overview of their functions and structures based on a wide range of integrated information.
SMART - Simple Modular Architecture Research Tool
SMART is a bioinformatics tool designed to identify and analyze functional domains in protein sequences. It is particularly useful for annotating and interpreting protein function, helping to understand the modular architecture of proteins and the importance of different domains.
Input
Choose the SMART mode: normal or genomic. After this, paste an amino acid with FASTA sequence or Bare sequence.
Algorithm
The SMART database integrates manually curated Hidden Markov Models for many domains with a powerful web-based interface with various analysis and visualization tools. SMART is also integrated and synchronized with the UniProt, Ensembl and STRING databases, for example, that increase the total number of annotated domains and other protein features.
Results
In summary, the integration of SMART with these databases allows for a more robust and detailed analysis of functional domains and motifs in proteins, providing a more complete understanding of protein structure and function.
The SMART algorithm combines sequence alignments, statistical modelling and HMMs to identify and analyze functional domains in proteins, providing a powerful tool for annotating and understanding protein structure and function.
deepTMHMM - A Deep Learning Model for Transmembrane Topology Prediction and Classification
DeepTMHMM is an algorithm based on a learning model that can detect and predict the topology of transmembrane proteins and is currently a more complete method with better performance for proteomics. Recently improved topology prediction, can detect the presence of signal peptides and can predict the topology of barrel-beta structures.
Input
Enter an amino acid FASTA sequence or file.
Algorithm
DeepTMHMM is based on a deep learning encoder-decoder sequence-to-sequence model that takes a protein sequence as input and outputs the corresponding sequence of labels per residue. The encoder representations are fed into a conditional random field (CRF). The CRF is closely related to the hidden Markov model used in TMHMM. The CRF decoder assigns a probability to the entire output sequence, rather than treating each position in the sequence as an independent classification task
Results
This program combines advanced deep learning techniques and Hidden Markov Models to provide accurate and detailed predictions of transmembrane helices in proteins. The integration of these approaches allows DeepTMHMM to capture complex patterns and provide valuable information about the structure and function of membrane proteins.
The DeepTMHMM program has the ability to handle complex data and provide detailed predictions, making it a valuable choice for researchers interested in understanding the structure and function of membrane proteins.
The Phobius is a bioinformatics tool for predicting transmembrane helices and peptide signals in protein sequences. It combines machine learning methods with statistical algorithms to provide information about the structure and localisation of important regions in proteins.
Input
Enter an amino acid FASTA sequence or file and select the output format (short or long with or without graphics).
Algorithm
Phobius uses Hidden Markov Models (HMMs) to make these predictions, combining statistical approaches and protein sequence features to identify important regions.
Results
Phobius can predict transmembrane helices and peptide signals based on patterns learned from known protein sequences. Its combined approach of machine learning and statistical modelling enables detailed and accurate analysis of protein sequences.
Phobius is able to provide detailed and integrated predictions of transmembrane helices and peptide signals, producing accurate and reliable results. This integrated approach and ease of use make it a valuable tool for protein analysis and functional annotation.
SCOPe - Structural Classification of Proteins — extended
The SCOPe database is a new version that can classify proteins based on their three-dimensional structure, grouping together proteins with similar structures and functions. The database provides an accurate, detailed and comprehensive description of the structural and evolutionary relationships between most proteins of known structure.
Input
On the tab “browse” and find a lineage for protein class, to obtain valuable data on the structure and classification.
Algorithm
SCOPe classifies many newer structures through a combination of automation and manual curation, aiming for the same accuracy as the hand-curated SCOP releases. SCOPe also incorporates and updates the Astral database.
Results
This database is a powerful tool that provides information about the organization and relationships between proteins based on their three-dimensional structures. Its key features include detailed hierarchical classification, access to structural data, prediction and analysis tools, and an accessible user interface. It is essential for bioinformatics and protein structure research,
SCOPe is a powerful tool for the classification and analysis of protein structures. You can gain insight into the structural relationships between proteins, explore their functions and integrate this information into biological and bioinformatics research.
CATH - Class, Architecture, Topology, and Homologous superfamily
CATH is a database that classifies protein structures, identifying and grouping protein domains based on their evolutionary relationships.
Input
You can search by keyword, PDB code, GO term and others. You can also search by FASTA format in 3D structure, protein evolution, protein function, conserved sites. You can also download data.
Algorithm
CATH use three-dimensional structures of proteins from databases such as the Protein Data Bank (PDB). CATH manually curates the database and some have developed algorithms to help with the automated aspects of maintenance and updating.
Results
CATH combines different computational methods to classify proteins into a detailed hierarchy and provides detailed and organized results on the structure and classification of proteins, which are essential for various bioinformatic and biological analyses.
This is an essential resource for the classification and structural analysis of proteins, providing a detailed insight into the organization and evolutionary relationships. With a hierarchy of classifications, it is a powerful tool for researchers interested in understanding the complexity of protein structures and their biological functions.