A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

“…Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. …”

Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics by Paolo Cifani (1575613)

“…Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses by Dominykas Lukauskis (14143149)

“…OpenBPMD is powered by the OpenMM simulation engine and uses a revised scoring function. The algorithm was validated by testing it on a wide range of targets and showing that it matches or exceeds the performance of the original BPMD. …”

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. …”

Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…<div><p>The most frequently used approach for protein structure prediction is currently homology modeling. …”

gmxapi usage examples. by M. Eric Irrgang (4487278)

PyPEFAn Integrated Framework for Data-Driven Protein Engineering by Niklas E. Siedhoff (11133851)

“…Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. …”

Integration of Morphoscanner with MDAnalysis, NGLview and Networkx. by Federico Fontana (533670)

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes by Raghvendra Agrawal (17135479)

“…This paper describes an implementation of an automation technique on the executable programs like ligplot.exe, hbplus.exe and hbadd.exe to obtain the 2D interaction map (LigPlots) of the protein and ligand complex (*.ps) and hydrogen bonds and hydrophobic interactions in *.csv format for molecules to be considered for virtual screening by using some sorting & searching algorithms and python’s file handling functions, and it also mentions the program’s limitations and availability of the program. …”

BrainPepPass: all scripts by Ewerton de Oliveira (16033631)

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Known compounds and new lessons: structural and electronic basis of flavonoid-based bioactivities by Rohan J. Meshram (6563189)

“…The current report thus focuses on providing an electronic explanation of these bioactivities using density functional theory-based quantum chemical descriptors. …”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…"dim_0" holds the row/index names as an array of strings, "dim_1" holds the column names as an array of strings, and "data" holds the matrix contents as a 2D array of floats. In python, these files can be read in with:</p><p dir="ltr"><br></p><p dir="ltr"> import pandas as pd</p><p dir="ltr"> import h5py</p><p dir="ltr"><br></p><p dir="ltr"> def read_hdf5(filename):</p><p dir="ltr"> src = h5py.File(filename, 'r')</p><p dir="ltr"> try:</p><p dir="ltr"> dim_0 = [x.decode('utf8') for x in src['dim_0']]</p><p dir="ltr"> dim_1 = [x.decode('utf8') for x in src['dim_1']]</p><p dir="ltr"> data = np.array(src['data'])</p><p dir="ltr"><br></p><p dir="ltr"> return pd.DataFrame(index=dim_0, columns=dim_1, data=data)</p><p dir="ltr"> finally:</p><p dir="ltr"> src.close()</p><p dir="ltr"><br></p><p>##################################################################</p><p dir="ltr">Files (not every dataset will have every type of file listed below):</p><p>##################################################################</p><p dir="ltr"><br></p><p dir="ltr">AllFeaturePredictions.hdf5: Matrix of cell lines by perturbations, with values indicating the predicted viability using a model with all feature types.…”