Identification of Structurally Related Antibodies in Antibody Sequence Databases Using Rosetta-Derived Position-Specific Scoring.
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ABSTRACT:
SUBMITTER: Finn JA
PROVIDER: S-EPMC7544681 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSequence matching is extremely important for applications throughout biology, particularly for discovering information such as functional and evolutionary relationships, and also for discriminating between unimportant and disease mutants. At present the functions of a large fraction of genes are unknown; improvements in sequence matching will improve gene annotations. Universal amino acid substitution matrices such as Blosum62 are used to measure sequence similarities and to identify distant homologues, regardless of the structure class. However, such single matrices do not take into account important structural information evident within the different topologies of proteins and treats substitutions within all protein folds identically. Others have suggested that the use of structural information can lead to significant improvements in sequence matching but this has not yet been very effective. Here we develop novel substitution matrices that include not only general sequence information but also have a topology specific component that is unique for each CATH topology. This novel feature of using a combination of sequence and structure information for each protein topology significantly improves the sequence matching scores for the sequence pairs tested. We have used a novel multi-structure alignment method for each homology level of CATH in order to extract topological information.ResultsWe obtain statistically significant improved sequence matching scores for 73 % of the alpha helical test cases. On average, 61 % of the test cases showed improvements in homology detection when structure information was incorporated into the substitution matrices. On average z-scores for homology detection are improved by more than 54 % for all cases, and some individual cases have z-scores more than twice those obtained using generic matrices. Our topology specific similarity matrices also outperform other traditional similarity matrices and single matrix based structure methods. When default amino acid substitution matrix in the Psi-blast algorithm is replaced by our structure-based matrices, the structure matching is significantly improved over conventional Psi-blast. It also outperforms results obtained for the corresponding HMM profiles generated for each topology.ConclusionsWe show that by incorporating topology-specific structure information in addition to sequence information into specific amino acid substitution matrices, the sequence matching scores and homology detection are significantly improved. Our topology specific similarity matrices outperform other traditional similarity matrices, single matrix based structure methods, also show improvement over conventional Psi-blast and HMM profile based methods in sequence matching. The results support the discriminatory ability of the new amino acid similarity matrices to distinguish between distant homologs and structurally dissimilar pairs.
| S-EPMC5006591 | biostudies-literature
Project description:HIV-1 is a retrovirus that integrates into host chromatin and can remain transcriptionally quiescent in a pool of immune cells. This characteristic enables HIV-1 to evade both host immune responses and antiretroviral drugs, leading to persistent infection. Upon reactivation of proviral gene expression, HIV-1 envelope (HIV-1 Env) glycoproteins are expressed on the cell surface, transforming latently infected cells into targets for HIV-1 Env-specific monoclonal antibodies (mAbs), which can engage immune effector cells to kill productively infected CD4+ T cells and thus limit the spread of progeny virus. Recent innovations in antibody engineering have resulted in novel immunotherapeutics such as bispecific dual-affinity re-targeting (DART) molecules and other bi- and trispecific antibody designs that can recognize HIV-1 Env and recruit cytotoxic effector cells to kill CD4+ T cells latently infected with HIV-1. Here, we review these immunotherapies, which are designed with the goal of curing HIV-1 infection.
| S-EPMC5549020 | biostudies-other
Project description:BACKGROUND: The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species. RESULTS: In this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli). CONCLUSIONS: Our pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.
| S-EPMC3549806 | biostudies-literature
Project description:BACKGROUND: Modern DNA sequencing methods produce vast amounts of data that often requires mapping to a reference genome. Most existing programs use the number of mismatches between the read and the genome as a measure of quality. This approach is without a statistical foundation and can for some data types result in many wrongly mapped reads. Here we present a probabilistic mapping method based on position-specific scoring matrices, which can take into account not only the quality scores of the reads but also user-specified models of evolution and data-specific biases. RESULTS: We show how evolution, data-specific biases, and sequencing errors are naturally dealt with probabilistically. Our method achieves better results than Bowtie and BWA on simulated and real ancient and PAR-CLIP reads, as well as on simulated reads from the AT rich organism P. falciparum, when modeling the biases of these data. For simulated Illumina reads, the method has consistently higher sensitivity for both single-end and paired-end data. We also show that our probabilistic approach can limit the problem of random matches from short reads of contamination and that it improves the mapping of real reads from one organism (D. melanogaster) to a related genome (D. simulans). CONCLUSION: The presented work is an implementation of a novel approach to short read mapping where quality scores, prior mismatch probabilities and mapping qualities are handled in a statistically sound manner. The resulting implementation provides not only a tool for biologists working with low quality and/or biased sequencing data but also a demonstration of the feasibility of using a probability based alignment method on real and simulated data sets.
| S-EPMC4021105 | biostudies-literature
Project description:?-amyloid oligomers are thought to be the most toxic species formed en route to fibril deposition in Alzheimer's disease. Transthyretin is a natural sequestering agent of ?-amyloid oligomers: the binding site to ?-amyloid has been traced to strands G/H of the inner ?-sheet of transthyretin. A linear peptide, with the same primary sequence as the ?-amyloid binding domain on transthyretin, was moderately effective at inhibiting ?-amyloid fibril growth. Insertion of a ?-turn template and cyclization greatly increased stability against proteolysis and improved efficacy as an amyloid inhibitor. However, the cyclic peptide still contained a significant amount of disorder. Using the Simple Cyclic Peptide Application within ROSETTA as an in silico predictor of cyclic peptide conformation and stability, we investigated putative structural enhancements, including stabilization by disulfide linkages and insertion of a second ?-turn template. Several candidates were synthesized and tested for secondary structure and ability to inhibit ?-amyloid aggregation. The results demonstrate that cyclization, ?-sheet structure and conformational homogeneity are all preferable design features, whereas disulfide bond formation across the two ?-strands is not preferable.
| S-EPMC7212263 | biostudies-literature
Project description:Antibody development is still associated with substantial risks and difficulties as single mutations can radically change molecule properties like thermodynamic stability, solubility or viscosity. Since antibody generation methodologies cannot select and optimize for molecule properties which are important for biotechnological applications, careful sequence analysis and optimization is necessary to develop antibodies that fulfil the ambitious requirements of future drugs. While efforts to grab the physical principles of undesired molecule properties from the very bottom are becoming increasingly powerful, the wealth of publically available antibody sequences provides an alternative way to develop early assessment strategies for antibodies using a statistical approach which is the objective of this paper. Here, publically available sequences were used to develop heuristic potentials for the framework regions of heavy and light chains of antibodies of human and murine origin. The potentials take into account position dependent probabilities of individual amino acids but also conditional probabilities which are inevitable for sequence assessment and optimization. It is shown that the potentials derived from human sequences clearly distinguish between human sequences and sequences from mice and, hence, can be used as a measure of humaness which compares a given sequence with the phenotypic pool of human sequences instead of comparing sequence identities to germline genes. Following this line, it is demonstrated that, using the developed potentials, humanization of an antibody can be described as a simple mathematical optimization problem and that the in-silico generated framework variants closely resemble native sequences in terms of predicted immunogenicity.
| S-EPMC3804498 | biostudies-literature
Project description:Many studies have used position-specific scoring matrices (PSSM) profiles to characterize residues in protein structures and to predict a broad range of protein features. Moreover, PSSM profiles of Protein Data Bank (PDB) entries have been recalculated in many works for different purposes. Although the computational cost of calculating a single PSSM profile is affordable, many statistical studies or machine learning-based methods used thousands of profiles to achieve their goals, thereby leading to a substantial increase of the computational cost. In this work we present a new database compiling PSSM profiles for the proteins of the PDB. Currently, the database contains 333,532 protein chain profiles involving 123,135 different PDB entries.
| S-EPMC6149929 | biostudies-literature
Project description:Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, often contain similar assortments of sequence-specific transcription factor (TF) binding sites. Knowledge of how these sequence motifs encode multiple, often overlapping gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease. Exploiting natural genetic variation, perturbation of endogenous TF protein levels, and analysis of synthetic regulatory elements using a novel transcription start site (TSS) capturing massively parallel reporter assay approach, here we show that the outcome of TF binding on transcription initiation is position dependent. Analyzing TF motif occurrences relative to the TSS, we identified several motifs with highly preferential and superhelical positioning. We show that these patterns are a combination of a TF's distinct functional profiles: many TFs, including canonical activators like NRF1, NFY, Sp1 or YY1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs can collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TFs, but with different spatial relationships, can result in distinct gene regulatory outcomes and underscore a critical role for TSS data in decoding the regulatory information of our genome.
2024-04-26 | GSE199431 | GEO
Project description:BackgroundMolecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet.ResultsIn this paper, we discuss an efficient and general method to compute the score distribution to any desired accuracy. The general approach may be applied to different sequence models and and various similarity measures that satisfy a few weak assumptions. We have access to the low-probability region ("tail") of the distribution where scores are larger than expected by pure chance and therefore relevant for practical applications. Our method uses recent ideas from rare-event simulations, combining Markov chain Monte Carlo simulations with importance sampling and generalized ensembles. We present results for the score statistics of fixed and random queries against random sequences. In a second step, we extend the approach to a model of transmembrane proteins, which can hardly be described as i.i.d. sequences. For this case, we compare the statistical properties of a fixed query model as well as a hidden Markov sequence model in connection with a position based scoring scheme against the classical approach.ConclusionsThe results illustrate that the sensitivity and specificity strongly depend on the underlying scoring and sequence model. A specific ROC analysis for the case of transmembrane proteins supports our observation.
| S-EPMC3042914 | biostudies-literature
Project description:MotivationEfficiently aligning sequences is a fundamental problem in bioinformatics. Many recent algorithms for computing alignments through Smith-Waterman-Gotoh dynamic programming (DP) exploit Single Instruction Multiple Data (SIMD) operations on modern CPUs for speed. However, these advances have largely ignored difficulties associated with efficiently handling complex scoring matrices or large gaps (insertions or deletions).ResultsWe propose a new SIMD-accelerated algorithm called Block Aligner for aligning nucleotide and protein sequences against other sequences or position-specific scoring matrices. We introduce a new paradigm that uses blocks in the DP matrix that greedily shift, grow, and shrink. This approach allows regions of the DP matrix to be adaptively computed. Our algorithm reaches over 5-10 times faster than some previous methods while incurring an error rate of less than 3% on protein and long read datasets, despite large gaps and low sequence identities.Availability and implementationOur algorithm is implemented for global, local, and X-drop alignments. It is available as a Rust library (with C bindings) at https://github.com/Daniel-Liu-c0deb0t/block-aligner.
| S-EPMC10457662 | biostudies-literature