Biology subjects

Jabeen, H.

Publications and source records attributed to Jabeen, H..

3 recordsLinked to original sources

bioRxiv2023

Electric fields determine carbapenemase activity in class A β-lactamases

Antimicrobial resistance is a public health crisis. Limited understanding of the catalytic drivers in resistance-mediating enzymes such as {beta}-lactamases hinders our ability to combat this crisis. Here, we dissect the catalytic contributions of active-site electric fields in class A {beta}-lactamases. We studied the enzymatic hydrolysis of a carbapenem antibiotic by QM/MM molecular dynamics simulations and quantified active-site fields with a custom-made script. We discovered that the fields correlate well with activity and identified seven positions, some distal, that distinguish efficient carbapenemases. Electric-field analysis may help predict the activity of {beta}-lactamases and guide antibiotic and enzyme design. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/565607v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@15ee41aorg.highwire.dtl.DTLVardef@d1a11corg.highwire.dtl.DTLVardef@a83bd7org.highwire.dtl.DTLVardef@f0abdf_HPS_FORMAT_FIGEXP M_FIG C_FIG Electric field script: www.github.com/bunzela/FieldTools

biochemistry↗

bioRxiv2021

Genomic miscellany and allelic frequencies of Plasmodium falciparum msp-1, msp-2 and glurp in parasite isolates

The genomic miscellany of malaria parasites can inform the intensity of transmission and identify potential deficiencies in malaria control programs. The aim of this study was to investigate the genomic miscellany, allele frequencies and multiplicity of infection (MOI) of P. falciparum. MethodsA total of 85 isolates from patients presenting to the local health centers with P. falciparum species were collected from 2017 to 2019. Parasite DNA was extracted from a total of 200 {micro}l whole blood per patient using the Qiagen DNA extraction kit according to manufactures instructions. The polymorphic region of msp-1, msp-2 and glurp loci were genotyped by using nested polymerase chain reactions followed by gel electrophoresis for fragment analysis. ResultsGenetic diversity and allelic frequencies of msp-1, msp-2 and glurp were identified in 85 blood samples. A total of 62 msp alleles were detected in which 30 for msp-1 and 32 for msp-2. For msp-1 the successful amplification occurred in (75/85) 88.23% isolates for msp-1, 78.9% (67/85) for msp-2 and 70% (60/85) for glurp. For msp-1, the K1 allelic family was predominant at 66.66% (50/75), followed by RO33 and MAD20. The frequency of samples having only K1, MAD20 and RO33 were 21.34% (16/75), 8% (6/75) and 10.67% (8/75) respectively. In msp-2, the FC27 allelic family was the most abundant with 70.14% (47/67) compared to 3D7 with 67.16% (45/67). Nine glurp RII region genotypes were identified. The overall mean multiplicity of infection was 2.6 with1.8 and 1.4 for msp-1 and msp-2 respectively while for glurp RII genes (MOI=1.03). There was no significant association between multiplicity of infection and age group (Spearmans rank coefficient = 0.050; P = 0.6). There was significant correlation between MOI and parasite density for msp-2 allelic family. ConclusionOur study showed high genetic diversity and allelic frequency with multiple clones of msp-1, msp-2 and glurp in P. falciparum isolates from malaria patients in Khyber Pakhtunkhwa Pakistan. In the present study the genotype data provided the valuable information which is essential for monitoring the impact of malaria eradication efforts in this region.

zoology↗

bioRxiv2018

BioKEEN: A library for learning and evaluating biological knowledge graph embeddings

Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programming and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.\n\nAvailabilityBioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN as well as through PyPI.

bioinformatics↗