Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.     

A new mixture model approach to analyzing allelic-loss data using Bayes factors

Background  

Allelic-loss studies record data on the loss of genetic material in tumor tissue relative to normal tissue at various loci along the genome. As the deletion of a tumor suppressor gene can lead to tumor development, one objective of these studies is to determine which, if any, chromosome arms harbor tumor suppressor genes.     

Bayes factors for detection of Quantitative Trait Loci

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.     

Conformational space comparison of GnRH and lGnRH-III using molecular dynamics, cluster analysis and Monte Carlo thermodynamic integration

The conformational space available to GnRH and lGnRH-III was compared using 5.2 ns constant temperature and pressure molecular dynamics simulations with explicit TIP3P solvation and the AMBER v. 5.0 force field. Cluster analysis of both trajectories resulted in two groups of conformations. Results of free energy calculations, in agreement with previous experimental data, indicate that a conformation with a turn from residues 5 through 8 is preferred for GnRH in an aqueous environment. By contrast, a conformation with a helix from residues 2 through 7 with a bend from residues 6 through 10 is preferred for lGnRH-III in an aqueous environment. The side chains of His2 and Trp3 in lGnRH-III occupy different regions of phase space and participate in weakly polar interactions different from those in GnRH. The unique conformational properties of lGnRH-III may account for its specific anti cancer activity.     

A thermodynamic model for force integration and microtubule assembly during axonal elongation

We present here a thermodynamic model for tension and compression forces within axons (neurites) of the specific neural-cell line, PC 12, which seems generally applicable to neuronal growth. We suggest that these forces play a crucial role in microtubule assembly during axonal elongation. The Gibbs free energy change for the axonal elongation phase of neuronal growth is modeled as the sum of the extensional work for pulling on a random actin network, work of assembly for compressed microtubules and surface energy terms. This model explains the results of previously published experiments concerning axonal stability and microtubule polymerization and has been used to predict other phenomena.     

Predicting the effects of basepair mutations in DNA-protein complexes by thermodynamic integration

Thermodynamically rigorous free energy methods in principle allow the exact computation of binding free energies in biological systems. Here, we use thermodynamic integration together with molecular dynamics simulations of a DNA-protein complex to compute relative binding free energies of a series of mutants of a protein-binding DNA operator sequence. A guanine-cytosine basepair that interacts strongly with the DNA-binding protein is mutated into adenine-thymine, cytosine-guanine, and thymine-adenine. It is shown that basepair mutations can be performed using a conservative protocol that gives error estimates of ∼10% of the change in free energy of binding. Despite the high CPU-time requirements, this work opens the exciting opportunity of being able to perform basepair scans to investigate protein-DNA binding specificity in great detail computationally.     

Measuring thermodynamic details of DNA hybridization using fluorescence

Modern real-time PCR systems make it easy to monitor fluorescence while temperature is varied for hundreds of samples in parallel, permitting high-throughput studies. We employed such system to investigate melting transitions of ordered nucleic acid structures into disordered random coils. Fluorescent dye and quencher were attached to oligonucleotides in such a way that changes of fluorescence intensity with temperature indicated progression of denaturation. When fluorescence melting data were compared with traditional ultraviolet optical experiments, commonly used dye/quencher combinations, like fluorescein and tetramethylrhodamine, showed substantial discrepancies. We have therefore screened 22 commercially available fluorophores and quenchers for their ability to reliably report annealing and melting transitions. Dependence of fluorescence on temperature and pH was also investigated. The optimal performance was observed using Texas Red or ROX dyes with Iowa Black RQ or Black Hole quenchers. These labels did not alter two-state nature of duplex melting process and provided accurate melting temperatures, free energies, enthalpies, and entropies. We also suggest a new strategy for determination of DNA duplex thermodynamics where concentration of a dye-labeled strand is kept constant and its complementary strand modified with a quencher is added at increasing excess. These methodological improvements will help build predictive models of nucleic acid hybridization.     

Computing free energies using nested sampling-based approaches

Abstract

Nested sampling (NS) has emerged as a powerful statistical mechanical sampling technique to compute the partition function of atomic and molecular systems. From the partition function all thermodynamic quantities can be calculated in absolute terms, including absolute free energies and entropies. In this article, we provide a brief overview of NS within a Bayesian context, as well as overviews of how NS is used to compute the partition functions and thermodynamic quantities in the canonical and isothermal-isobaric ensembles. Then we introduce a new scheme, Coupling Parameter Path Nested Sampling, to estimate the free energy difference between two systems with different potential energy functions. The method uses a NS simulation to traverse the same path through phase space as would be covered in traditional coupling parameter-based methods such as thermodynamic integration and perturbation approaches. We demonstrate the new method with two case studies and confirm its accuracy by comparison to conventional methods, including Widom test particle insertion and thermodynamic integration. The proposed method provides a powerful alternative to traditional coupling parameter-based free energy simulation methods.