Biology subjects

Pang, Y.-P.

Publications and source records attributed to Pang, Y.-P..

3 recordsLinked to original sources

bioRxiv2018

Peptide-binding groove contraction linked to the lack of T-cell response: Using complex structure and energy to identify neoantigens

Using personalized peptide vaccines (PPVs) to target tumor-specific non-self antigens (neoantigens) is a promising approach to cancer treatment. However, the development of PPVs is hindered by the challenge of identifying tumor-specific neoantigens, in part because current in silico methods for identifying such neoantigens have limited effectiveness. Here we report the results of molecular dynamics simulations of 12 oligopeptides bound with a human leukocyte antigen (HLA), revealing a previously unrecognized association between the inability of an oligopeptide to elicit a T-cell response and the contraction of the peptide-binding groove upon binding of the oligopeptide to the HLA. Our conformational analysis showed that this association was due to incompatibility at the interface between the contracted groove and its {beta}-T-cell antigen receptor (TCR). This structural demonstration that having the capability to bind HLA does not guarantee immunogenicity prompted us to develop an atom-based method [Formula] to predict immunogenicity through using the structure and energy of a peptide*HLA complex to assess the propensity of the complex for forming a ternary complex with its TCR. In predicting the immunogenicities of the 12 oligopeptides, [Formula] achieved a 100% success rate compared with success rates of 25-50% for 11 publicly available residue-based methods including NetMHC-4.0. While further validation and refinements of [Formula] are required, our results suggest a need to develop in silico methods that assess peptide characteristics beyond their capability to form stable binary complexes with HLAs to help remove hurdles in using the patient tumor DNA information to develop PPVs for personalized cancer immunotherapy.

immunology↗

bioRxiv2017

On The Use Of The Experimentally Determined Enzyme Inhibition Constant As A Measure Of Absolute Binding Affinity

Defined as a state function representing an inhibitors absolute affinity for its target enzyme, the experimentally determined enzyme inhibition constant (Ki) is widely used to rank order binding affinities of different inhibitors for a common enzyme or different enzymes for a common inhibitor and to benchmark computational approaches to predicting binding affinity. Herein, we report that adsorption of bis(7)-tacrine to the glass container surface increased its Ki against Electrophorus electricus acetylcholinesterase (eeAChE) to 3.2 {+/-} 0.1 nM (n = 5) compared to 2.9 {+/-} 0.4 pM (n = 5) that was determined using plastic containers with other assay conditions kept the same. We also report that, due to binding or \"adsorption\" of bis(7)-tacrine to the inactive eeAChE, the bis(7)-tacrine Ki increased from 2.9 {+/-} 0.4 pM (n = 5) to 734 {+/-} 70 pM (n = 5) as the specific eeAChE activity decreased from 342 U/mg to 26 U/mg while other assay conditions were kept the same. These results caution against using Kis to rank order binding potencies, define selectivity, or benchmark computational methods without knowing detailed assay conditions.\n\nAbbreviations

biophysics↗

bioRxiv2016

How fast can fast-folding proteins autonomously fold in silico?

In reported microcanonical molecular dynamics simulations, fast-folding proteins CLN025 and Trp-cage autonomously folded to experimentally determined native conformations. However, the folding times of these proteins derived from the simulations were more than 4-10 times longer than their experimental values. This article reports autonomous folding of CLN025 and Trp-cage in isobaric-isothermal molecular dynamics simulations with agreements within factors of 0.69-1.75 between simulated and experimental folding times at different temperatures. These results show that CLN025 and Trp-cage can now autonomously fold in silico as fast as in experiments, and suggest that the accuracy of folding simulations for fast-folding proteins begins to overlap with the accuracy of folding experiments. This opens new prospects of developing computer algorithms that can predict both ensembles of conformations and their interconversion rates for a protein from its sequence for artificial intelligence on how and when a protein acts as a receiver, switch, and relay to facilitate various subcellular-to-tissue communications. Then the genetic information that encodes proteins can be better read in the context of intricate biological functions.

biophysics↗