1,720,998 research outputs found
protease_sequences_1998_to_2006
No full textHIV-1 protease sequences collected from HIV Stanford Database on 17th September 2013. The data spans 9 years (1998-2006) and sequences from treated (i.e. patients receiving one or more protease inhibitors) and untreated patients are in separate files
longitudinal_protease_sequences
No full textHIV-1 protease sequences collected from HIV Stanford Database on 17 September 2013. The data represents HIV-1 protease sequences collected from same patients 'before' and 'after' a condition (i.e. 'treated' or 'untreated'). For example: sequences from patients who were initially untreated and then went on to receive treatment are titled "untreated_to_treated_protease_17SEP2013.src.blue.before.consensus_added" and "untreated_to_treated_protease_17SEP2013.src.blue.after.consensus_adde" respectively
sj-pdf-1-vmj-10.1177_1358863X231205574 – Supplemental material for Impact of preexisting coronary artery and peripheral artery disease on outcomes in diabetic patients after kidney transplant
No full textSupplemental material, sj-pdf-1-vmj-10.1177_1358863X231205574 for Impact of preexisting coronary artery and peripheral artery disease on outcomes in diabetic patients after kidney transplant by Sania Jiwani, Wan-Chi Chan, Monil Majmundar, Kunal N Patel, Harsh Mehta, Aditya Sharma, Gaurav Parmar, Mark Wiley, Peter Tadros, Eric Hockstad, Sri G Yarlagadda, Aditi Gupta and Kamal Gupta in Vascular Medicine</p
Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein.
No full textEpistatic interactions between residues determine a protein's adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the "fossils" of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing environment
Two-loci two-allele model.
No full textThe left panel shows the fitness landscapes and epistasis given by Eq (9) in the first and second half of the simulation (updates 0–499: w0 = 1 and w1 = w2 = w3 = 10−5 ≈ 0; updates 500–1000: w0 = w3 = 1 and w1 = w2 = 10−5 ≈ 0). The xy-plane shows the four genotypes while the z-axis shows genotype fitness. The middle panel shows the genotype probabilities while the right panel shows the mutual information during the course of the simulation. Note that the increase in epistasis at the 500th update is reflected in the increase in mutual information. The mutation rate was 0.1 and starting population frequencies were p0 = 1 and p1 = p2 = p3 = 0.</p
Rates of mutation between four different genotypes.
No full textThe types are denoted as 0 = AA, 1 = Aa, 2 = aA, and 3 = aa.</p
Increase in epistasis in HIV-1 protease over time.
No full textPairwise interactions in the HIV-1 protease are shown for years 1998, 2002, and 2006 in the drug-free (top row) and drug environment (bottom row). Each heatmap shows the mutual information for each pair of residues. Pairwise information (and thus epistatic effects) are fairly constant in the drug-free environment, but gradually increase in the treated group.</p
Allele frequencies as fitness for type 3 (aa) is varied.
No full textIn this valley-crossing landscape, w0 is always 1 and w1 = w2 = 0. Plot shows allele frequencies pi at mutation rate μ = 0.1 as a function of w3. The intermediate types aA and Aa occur only at the rate of mutation as they have zero fitness.</p
- …
