HHblits is a bioinformatics tool for searching protein sequences. It identifies subtle or hidden similarities between proteins. By uncovering these connections, HHblits helps scientists understand protein function and evolution.
Uncovering Hidden Protein Connections
Proteins are long chains of amino acids that fold into specific three-dimensional structures, with their unique sequence dictating their biological role. Protein homology refers to the shared ancestry between proteins, meaning they originated from a common evolutionary ancestor. While some homologous proteins share obvious sequence similarities, many have diverged significantly over millions of years, making their relationship difficult to detect through simple comparisons. This challenge is known as “remote homology” detection, where proteins have very distant evolutionary ties and their amino acid sequences might show little direct resemblance.
HHblits addresses this challenge by employing an approach using Hidden Markov Models (HMMs). Instead of comparing individual protein sequences directly, HHblits builds HMMs that represent entire families of related proteins. These HMMs capture the patterns of amino acid conservation and variation within a protein family, creating a statistical profile. The tool then compares these HMM profiles against a database of other HMMs, a process known as HMM-HMM alignment.
The search process in HHblits is iterative. Initially, HHblits generates an HMM from a query protein sequence or a small set of related sequences. It then uses this HMM to search large protein databases, such as UniProt. Any significant matches found in one iteration are added to the initial set, and a new, more refined HMM is constructed for the next search round. This iterative refinement allows HHblits to detect weak similarities that simpler methods might miss, progressively revealing more distant protein relatives.
Why HHblits Stands Out
HHblits offers advantages over older or simpler protein sequence search methods, such as BLAST and PSI-BLAST. Its strength is its high sensitivity for detecting remote homologies. This means HHblits can identify distantly related proteins that share a common evolutionary origin, even when their amino acid sequences have diverged significantly and direct sequence comparison methods fail to find a match. For instance, it has been shown to have 50-100% higher sensitivity compared to PSI-BLAST.
The high sensitivity of HHblits stems from its use of HMM-HMM alignment, which is a more advanced comparison method than sequence-sequence or profile-sequence alignments used by other tools. By comparing profiles that encapsulate the evolutionary information of entire protein families, HHblits can discern subtle patterns of conservation that indicate distant relationships. This capability leads to more accurate predictions about a protein’s structure and function, even for proteins with previously unknown roles.
Beyond sensitivity, HHblits also demonstrates speed and efficiency, making it practical for analyzing large datasets. While profile-profile alignment methods were historically too slow for large databases, HHblits incorporates a discretized-profile prefilter that significantly reduces the number of full HMM-HMM alignments needed. This optimization allows HHblits to be faster than PSI-BLAST, often twice as fast, and sometimes 4-5 times faster than HMMER3 for iterative searches through databases like UniProt. The combination of high sensitivity and efficient processing makes HHblits a valuable tool for large-scale bioinformatics projects.
Impact on Understanding Life
HHblits has applications across various fields of biology and medicine. One application is predicting the three-dimensional structure of proteins. By identifying homologous proteins with known structures, HHblits provides templates that can be used to model the structure of uncharacterized proteins, a foundational step in drug design. This structural information is then used to design drugs that specifically interact with target proteins.
The tool also helps scientists determine the function of proteins with unknown roles. If HHblits finds a strong remote homology between an uncharacterized protein and a protein with a known function, it provides an indication that the unknown protein may perform a similar role. This is useful in genome annotation projects, where millions of newly sequenced proteins need to be assigned functions.
HHblits contributes to mapping evolutionary relationships between organisms. By detecting distant protein homologies, researchers can trace the evolutionary history of protein families and understand how different species are related at a molecular level. This deepens our understanding of life’s diversity and common ancestry. Identifying protein relationships also aids in understanding disease mechanisms by finding human proteins related to viral or bacterial proteins, potentially revealing new targets for therapeutic interventions.