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TwinCons: Conservation score for uncovering deep sequence similarity and divergence

ABSTRACT: We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a ‘cost’ of transforming one group to the other at each position of the alignment. The output distinguishes conserved, variable and signature positions. A signature is conserved within groups but differs between groups. The method automatically detects continuous characteristic stretches (segments) within alignments. TwinCons provides a convenient representation of conserved, variable and signature positions as a single score, enabling the structural mapping and visualization of these characteristics. Structure is more conserved than sequence. TwinCons highlights alternative sequences of conserved structures. Using TwinCons, we detected highly similar segments between proteins from the translation and transcription systems. TwinCons detects conserved residues within regions of high functional importance for the ribosomal RNA (rRNA) and demonstrates that signatures are not confined to specific regions but are distributed across the rRNA structure. The ability to evaluate both nucleic acid and protein alignments allows TwinCons to be used in combined sequence and structural analysis of signatures and conservation in rRNA and in ribosomal proteins (rProteins). TwinCons detects a strong sequence conservation signal between bacterial and archaeal rProteins related by circular permutation. This conserved sequence is structurally colocalized with conserved rRNA, indicated by TwinCons scores of rRNA alignments of bacterial and archaeal groups. This combined analysis revealed deep co-evolution of rRNA and rProtein buried within the deepest branching points in the tree of life. Author summary All species on Earth can be thought of as leaves on the Tree of Life, which are connected by branches representing their ancestral relationships. Biopolymers are evolutionary markers within species, that contain records of evolutionary history. Excavation of molecular evolutionary histories involves collecting sequences from extant species and organizing them into multiple sequence alignments. For the purpose of comparison, the sequences within an alignment can be partitioned into two groups, resulting in a composite alignment. We have developed the program TwinCons, to detect noisy signals of deep ancestry. TwinCons distinguishes conserved, variable and signature positions between the groups of the composite alignment. A signature is a position conserved within each group but differing between groups. TwinCons can further be used to detect uninterrupted ranges of positions (segments) preserved within the composite alignment. TwinCons results can be mapped onto structures of molecules. TwinCons scores can be applied to either proteins or ribonucleic acids (RNA). Using TwinCons we detected highly similar segments across ancient and essential protein components of living cells (translation and transcription) and pinpointed the deepest signatures between bacterial and archaeal RNAs within the ribosome.

SUBMITTER: Penev P

PROVIDER: S-EPMC8580257 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. RESULTS: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure. CONCLUSION: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

Project description:Pathogens usually evade and manipulate host-immune pathways through pathogen-host protein-protein interactions (PPIs) to avoid being killed by the host immune system. Therefore, uncovering pathogen-host PPIs is critical for determining the mechanisms underlying pathogen infection and survival. In this study, we developed a computational method, which we named pairwise structure similarity (PSS)-PPI, to predict pathogen-host PPIs. First, a high-quality and non-redundant structure-structure interaction (SSI) template library was constructed by exhaustively exploring heteromeric protein complex structures in the PDB database. New interactions were then predicted by searching for PSS with complex structures in the SSI template library. A quantitative score named the PSS score, which integrated structure similarity and residue-residue contact-coverage information, was used to describe the overall similarity of each predicted interaction with the corresponding SSI template. Notably, PSS-PPI yielded experimentally confirmed pathogen-host PPIs of human immunodeficiency virus type 1 (HIV-1) with performance close to that of in vitro high-throughput screening approaches. Finally, a pathogen-host PPI network of human pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, was constructed using PSS-PPI and refined using filtration steps based on cellular localization information. Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis. M. tuberculosis also targeted host factors known to regulate HIV replication. Taken together, our findings provide insights into the survival mechanisms of M. tuberculosis in human hosts, as well as co-infection of tuberculosis and HIV. With the rapid pace of three-dimensional protein structure discovery, the SSI template library we constructed and the PSS-PPI method we devised can be used to uncover new pathogen-host PPIs in the future.

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TwinCons: Conservation score for uncovering deep sequence similarity and divergence

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