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Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Dynamic matching technique computes sequence-similarity based upon popular matrices such as BLOSUM (BLOcks SUbstitution Matrix) and PAM (Protein Alignment Matrix). These matrices are based upon matching of the biochemical and biophysical properties of amino-acids. The match-score is positive for amino-acids having similar properties and negative for dissimilar properties. A specific popular substitution matrix is BLOSUM62 (see Table 10.3). As shown in Tables 10.2 and 10.3, similar amino-acid such as leucine and isoleucine have a similarity-core of +2; dissimilar amino-acids such as leucine and histidine have a similarity score of −4. There are many variations of BLOSUM and PAM matrices such as BLOSUM-50, BLOSUM-62, BLOSUM-80, PAM-30 and PAM-70.
Cloning, characterization, and inhibition of the novel β-carbonic anhydrase from parasitic blood fluke, Schistosoma mansoni
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2023
Susanna Haapanen, Andrea Angeli, Martti Tolvanen, Reza Zolfaghari Emameh, Claudiu T. Supuran, Seppo Parkkila
In order to visualise sequence conservation in metazoan β-CAs for this article, we collected a new sequence set on 30 March 2022. S. japonicum KAH8855123.1 was used as a query sequence in BLAST at NCBI, with substitution matrix BLOSUM45, result list size of 5000, and taxonomy filter set to Metazoa. This resulted in 643 hits, filtered (with NCBI Blast result tools) for 85 % query coverage, leaving 520 sequences (https://github.com/MarttiT/S.-mansoni-BCA/blob/main/SmaBCA520%20blast%20hits%20Descriptions.xlsx). Incidentally, at this point, the RefSeq version of S. mansoni β-CA (XP_018647616.1) was too short to pass the coverage filter. The longest 35 sequences (of 998–2153 aa, all from rotifers) were removed. The remaining 485 sequences were aligned preliminarily using the Clustal Omega48,49 within SeaView 5.0.4.,50 and sequences with unique insertions or clearly mismatching sequences within conserved regions were manually deleted (using SeaView). The remaining 390 sequences were realigned with SeaView, and the resulting MSA (https://github.com/MarttiT/S.-mansoni-BCA/blob/main/SeaView%20MSA%20390.aln) was filtered for sequences with more than 90 % identity against any other sequence in the set. The Decrease redundancy tool at https://web.expasy.org/decrease_redundancy/ was used for this purpose (Cédric Notredame, unpublished), and a final realignment was performed on the obtained set of 162 protein sequences with Clustal Omega at EBI (https://www.ebi.ac.uk/Tools/msa/clustalo/)49 (Supplementary File 1).
Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy
Published in OncoImmunology, 2021
Peng Bai, Yongzheng Li, Qiuping Zhou, Jiaqi Xia, Peng-Cheng Wei, Hexiang Deng, Min Wu, Sanny K. Chan, John W. Kappler, Yu Zhou, Eric Tran, Philippa Marrack, Lei Yin
Next, we sought to identify amino acid biochemical properties that define immunogenicity of neoantigens within the category of the “TCR-contacting mutations”. We evaluated four biochemical properties of amino acids, including “amino acid dissimilarity”, “hydrophobicity”, “polarity”, and “side chain bulkiness”, that discriminate between immunogenic and ineffective cohorts. First, we defined dissimilarity of peptide pairs (the phrase “peptide pairs” refers to the mutant and corresponding wild-type peptide), both for immunogenic and ineffective data, by using normalized BLOSUM50 substitution matrix.30 Next, “hydrophobicity”, “polarity”, and “side chain bulkiness” properties were analyzed for mutations across immunogenic and ineffective cohorts using independent numeric scales described by Chowell et al. (Supplementary Table. 4).31 We found no significant difference for the four properties between immunogenic and ineffective cohorts (Supplementary Fig. 1). Thus, in our datasets, we found that these four biochemical properties were not predictive for neoantigen immunogenicity.
Process optimization and protein engineering mitigated manufacturing challenges of a monoclonal antibody with liquid-liquid phase separation issue by disrupting inter-molecule electrostatic interactions
Published in mAbs, 2019
Qun Du, Melissa Damschroder, Timothy M. Pabst, Alan K. Hunter, William K. Wang, Haibin Luo
The homology model of the mAb-X and mAb-Y variable domains (Fv) was constructed with Molecular Operation Environment (MOE) version 2016.08 (Chemical Computing Group, Inc., [CCG], Montreal, Canada). The MOE numbering scheme (CCG), which combines the loop definitions of the Kabat scheme with the residue numbering of the Chothia scheme,26,27 was used for antibody numbering. Following homology model construction, the VH and VL sequences of mAb-X were used to search the Fab Database from the Protein Data Bank (PDB) structures for suitable templates. Sequence alignment scores for both the framework and the CDR were calculated according to the BLOSUM62 amino acid substitution matrix.27 CDR loops were calculated as similarity, and the structure score (geometric mean) of an antibody subdomain was calculated as follows: structure score = topology × geometry × (0.7 × B-Factors) × (0.65 × occupancy) × (0.75 × Resolution) × (0.65 R/Rfree).27