Dissertation presentation by Ryan melvin

Ryan Melvin final presentation, an overview of his physics research

Machine Learning Operations (MLOps) Cookiecutter Template

* - Authors contributed equally to this work This software is a cookie cutter template to expedite and streamline your next collaborative mahince learning (ML) and Artificial Intelligence (AI) project. This suite of tools integrates many principles of best-practice for developing ML and models to support development, production, debugging, package management, data traceability and more. Additional details are available at the corresponding GitHub for more information.  https://github.com/UABPeriopAI/MLOpsTemplate</p

HDBSCAN and Amorim-Hennig for MD

Scripts for performing first-pass non-parametric clustering on Molecular Dynamics trajectories using HDBSCAN and ntelligent Minkowski-Weighted K-Means (iMWK-means) with explicit rescaling followed by K-Means. These scripts depend on code from https://sourceforge.net/projects/unsupervisedpy/ and https://github.com/lmcinnes/hdbsca

Python Implementation of Quality Threshold Clustering for Molecular Dynamics

A python implementation of Heyer, 1999's Quality Threshold clustering algorithm specialized for molecular dynamics trajectories

Combining Molecular Dynamics and Biopolymer Docking

<p>Presentation given at  2nd Workshop on High-Throughput Molecular Dynamics 2015 at the Barcelona biomedical research park </p> <p>Abstract:</p> <p>High-throughput docking is most reliable when no structural change occurs in the receptor (i.e., rigid docking). However, in vivo (and in vitro) ligand binding and protein-protein interactions usually result in conformational change. To determine if we can overcome the limits of current docking software, we have performed ensemble docking runs, gathering sets of receptor and ligand conformations from microsecond-timescale all-atom molecular dynamics simulations. Here we provide insights and guidelines for others who plan to do likewise. As a case study, we present results from ensemble docking of a therapeutic polymer of FdUMP (5-fluoro-2′-deoxyuridine-5′-O-monophosphate) – a topoisomerase-1 (Top1), apoptosis-inducing poison – to Albumin and Vitronectin, showing that partially folded states of the FdUMP strand have the lowest free energy of binding to both proteins. We also present an example of this method applied to protein-protein interactions with two nucleotide binding proteins from Bacillus subtilis along with their interactions with ATP in complex. From these latter investigations, we predict a 3D structure in good agreement with results from residue mutation experiments. Finally, we propose a methodology for visualizing the uncertainty in these data – a method that can be applied to computational biology results in general.</p> <p> </p

VisualStatistics

<p>Python scripts for visualizing macromolecules and uncertainty</p

Melvin and Godwin Wake Forest University Physics Seminar 01/13/16

Ryan Melvin and Ryan Godwin's WFU Physics Department Seminar showing their research in the Salsbury Grou

Sufficient Sampling Correlation Code and Examples

Accompanying a pending PRE submission. Here we present a novel time-dependent correlation method that provides insight into how long a system takes to grow into its equal-time (Pearson) correlation. We also show a novel usage of an extant time-lagged correlation method that indicates the time for parts of a system to become decorrelated, relative to equal-time correlation. Given a completed simulation (or set of simulations), these tools estimate (1) how long of a simulation of the same system would be sufficient to observe the same correlated motions, (2) if patterns of observed correlated motions indicate events beyond the timescale of the simulation, and (3) how long of a simulation is needed to observe these longer timescale events. We view this method as a decision-support tool that will aid researchers in determining necessary sampling times. In principle, this tool is extendable to any multi-dimensional time series data with a notion of correlated fluctuations; however, here we limit our discussion to data from Molecular Dynamics simulations

Markov Cluster Analysis in Matlab

<p>Cluster Analysis is a set of codependent matlab functions that take cluster data as an input and outputs several plots using Markov analysis.</p> <p>CLUSTERANALYSIS will return multiple plots based on a set of clustering data. It calls the functions listed below and returns the plots described. It also returns a Markov time series and rate matrix. Cutoff is the cutoff for determining a transition from an initial pure state to another state. To simply pick the most likely state at each decision point, use 0. Steps is the maximum number of steps used by Pathways to plot transitions and TimeToEquilibrium to plot the difference in rate matrix-predicted populations from the actual cluster populations.</p> <p>Its underlying functions are</p> <p>CLUSTERPOPULATIONS calculates populations (as a whole number count) and equilibrium probability distribution.</p> <p>CLUSTERTIMESERIES takes each row and turn them into a set of vertically concatonated columns. Assign each frame a cluster number.</p> <p>MARKOVRATEMATRIX calculate a row-normalized rate matrix given a set of cluster numbers and corresponding frames in the form [frames cluster#]</p> <p>PATHWAYS Returns the pathway of a pure state over "maxTimeSteps" of a simulation using a Markov "rateMatrix." A transition out of a pure state is defined by minimum "cutoff" of how likely the system is to have left a pure state. Cutoff is the likelihood a state has transitioned OUT OF a pure state. If the cutoff is met but the original state is remains the most likely, a transition will NOT be recorded.</p> <p>TIMETOEQUILIBRIUM plots the difference between the populations predicted by the Markov rate matrix and the equilibrium probability distribution calculated from the original trajectory as a function of steps.</p

Scripts and Data for MSH26 Damage Response article

Scripts for reproducing work in article "MutSa's Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning" by Melvin, et al