Article
Biochemical Research Methods
Jan Ludwiczak, Aleksander Winski, Stanislaw Dunin-Horkawicz
Summary: This study developed localpdb, a versatile Python library for managing protein structures and annotations. It features a flexible plugin system for unifying structural data with diverse auxiliary resources, suitable for bioinformatic tasks, especially large-scale protein structural analysis and machine learning.
Article
Physics, Multidisciplinary
Cheng-Jun Xia, Toshiki Maruyama, Ang Li, Bao Yuan Sun, Wen-Hui Long, Ying-Xun Zhang
Summary: In this study, the equations of state (EOSs) and microscopic structures of neutron star matter in a wide density range were systematically investigated using the Thomas-Fermi approximation. It was found that the EOSs generally coincide at low and moderate densities, while they are sensitive to the effective interactions between nucleons at other density regions. The results have important implications for the study of the structures and evolutions of compact stars.
COMMUNICATIONS IN THEORETICAL PHYSICS
(2022)
Article
Chemistry, Medicinal
Anna M. Diaz-Rovira, Helena Martin, Thijs Beuming, Lucia Diaz, Victor Guallar, Soumya S. Ray
Summary: Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have had a significant impact on the field of structural biology and sparked discussions about their potential role in drug discovery. However, there have been few studies addressing the use of these models in virtual screening, especially with low prior structural information. In this study, we developed an AlphaFold2 version that excludes structural templates with more than 30% sequence identity, and found that using these structures directly may not be ideal for virtual screening campaigns, suggesting the need for post-processing modeling to generate more realistic binding site models.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Multidisciplinary
Ogonna Nwajiobi, Sriram Mahesh, Xavier Streety, Monika Raj
Summary: In this study, a chemical technology termed as STaR was used to selectively triazenation reaction of secondary amines using arene diazonium salts under biocompatible conditions, achieving highly selective, rapid, and robust tagging of Kme peptides. The efficient decoupling of the triazene-conjugate to afford unmodified starting components under mild conditions was highlighted, establishing a unique chemoselective bioconjugation strategy for the selective enrichment of Kme PTMs.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2021)
Article
Chemistry, Physical
Eric J. M. Lang, Emily G. Baker, Derek N. Woolfson, Adrian J. Mulholland
Summary: We tested a range of standard GB models and protein force fields on a set of designed peptides and found that none of the models accurately predicted the a-helical content for all peptides. These peptides serve as a useful test set for simulation methods.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Automation & Control Systems
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca
Summary: Alibi Explain is an open-source Python library for explaining machine learning model predictions. It features state-of-the-art explainability algorithms for classification and regression models, covering multiple data types and explanation scopes. The library aims to be a production-ready toolkit with integrations into machine learning deployment platforms and distributed explanation capabilities.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Dominik Gehringer, Martin Friak, David Holec
Summary: We present a Python package for generating special quasi-random structures (SQS) for atomic-scale calculations of disordered systems. The package offers efficient optimization methods and analysis tools for finding optimal structures and quantifying randomness. It also provides a command-line interface and Python API for easy integration into complex simulation workflows.
COMPUTER PHYSICS COMMUNICATIONS
(2023)
Article
Biochemical Research Methods
Bosheng Song, Xiaoyan Luo, Xiaoli Luo, Yuansheng Liu, Zhangming Niu, Xiangxiang Zeng
Summary: The spatial structures of proteins are important for their functions, but the limited quantity of known protein structures restricts their application in prediction methods. Utilizing predicted protein structure information can improve sequence-based prediction methods. TAGPPI is a novel framework that uses only protein sequences to predict protein-protein interactions and extracts spatial structure information from contact maps to improve prediction performance.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Monika Bokor, Agnes Tantos
Summary: Protein secondary structure predictions were compared with experimental results, revealing that thymosin-beta(4) and stabilin-2 cytoplasmic domain are mainly disordered, consistent with experiments. Alpha-synuclein A53T mutant showed less predicted disorder compared to other variants, contrary to experimental NMR results.
JOURNAL OF PROTEOME RESEARCH
(2021)
Article
Engineering, Industrial
Marlene Kuhn, Felix Funk, Guanlai Zhang, Joerg Franke
Summary: Production processes are becoming increasingly complex and decentralized, posing challenges for the development of traceability systems. While some solutions exist, customized and volatile industries lack appropriate models and proofs of concept.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Instruments & Instrumentation
Jaroslav Adam, Michael G. Cherney, Joey D'Alesio, Emma Dufresne, Lukas Holub, Janet E. Seger, David Tlusty
Summary: The STAR experiment has delivered significant physics results for over 20 years with a controls system based on EPICS, which is now being replaced with a Python-based approach for improved stability and documentation. This paper introduces the experiment, EPICS architecture, and the use of Python for control software, with specific examples and UI upgrades outlined in subsequent sections.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
(2021)
Article
Chemistry, Medicinal
Thijs Beuming, Helena Martin, Anna M. Diaz-Rovira, Lucia Diaz, Victor Guallar, Soumya S. Ray
Summary: The availability of AlphaFold2 has generated excitement in the scientific community, especially among drug researchers, due to its high accuracy in predicting protein structures. This study explores whether the predicted structures by AlphaFold2 are accurate enough to be useful in computationally driven drug discovery programs, specifically in predicting ligand binding sites. The findings suggest that, under certain circumstances, AlphaFold2-modeled structures can be used with physics-based methods, such as free energy perturbation, in the lead optimization stages of drug discovery programs.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Biochemical Research Methods
Franco Pradelli, Giovanni Minervini, Silvio C. E. Tosatto
Summary: Mathematical models are effective in studying cancer development, and Phase Field Models (PFMs) can accurately simulate cancer growth and related phenomena. However, the implementation of such models is rarely published, limiting access to these techniques. To bridge this gap, we have developed an open-source Python package called Mocafe, which implements some important PFMs reported in the literature and is designed to be extensible.
Article
Computer Science, Artificial Intelligence
Linlin Jia, Benoit Gauzere, Paul Honeine
Summary: This paper introduces graphkit-learn, the first Python library for efficient computation of graph kernels based on linear patterns. It provides strategies to address the computational complexity issue and experiments show the relevance of the proposed library.
PATTERN RECOGNITION LETTERS
(2021)
Article
Mathematics
Zhenkun Zhang, Hongjian Lai
Summary: The cutwidth of a graph G is the smallest integer k such that the vertices can be arranged in a linear layout with at most k edges crossing between consecutive vertices. The cutwidth problem is to determine this value for a given graph. A graph with cutwidth k is k-cutwidth critical if every proper subgraph has a cutwidth less than k and the graph is homeomorphically minimal. In this paper, 4-cutwidth critical graphs, except for five irregular ones, are classified into two classes: those with a central vertex and those with a central cycle of length six. Both classes can be decomposed into subgraphs with cardinality 2, 3, or 4, where each subgraph is either a 2-cutwidth or a 3-cutwidth graph.
Article
Biochemistry & Molecular Biology
Ivo E. Sampaio-Dias, Jose E. Rodriguez-Borges, Victor Yanez-Perez, Sonia Arrasate, Javier Llorente, Jose M. Brea, Harbil Bediaga, Dolores Vina, Maria Isabel Loza, Olga Caamano, Xerardo Garcia-Mera, Humberto Gonzalez-Diaz
Summary: The synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D-2 modulating agents were described in this work. Peptidomimetic 6a showed promising results without neurotoxicity at high concentrations, making it a potential lead compound for further development. Additionally, the ALLOPTML model, based on perturbation theory and machine learning, demonstrated high specificity, sensitivity, and accuracy in predicting the allosteric modulatory potential of molecular candidates.
ACS CHEMICAL NEUROSCIENCE
(2021)
Article
Chemistry, Medicinal
Karel Dieguez-Santana, Gerardo M. Casanola-Martin, James R. Green, Bakhtiyor Rasulev, Humberto Gonzalez-Diaz
Summary: This study developed a CPTML model for MRNs of multiple organisms using Combinatorial Perturbation Theory and Machine Learning techniques, and identified PTML models based on Bayesian network, Decision Tree, and Random Forest algorithms as the three best non-linear models with high accuracy.
CURRENT TOPICS IN MEDICINAL CHEMISTRY
(2021)
Editorial Material
Chemistry, Medicinal
Humberto Gonzalez-Diaz
CURRENT TOPICS IN MEDICINAL CHEMISTRY
(2021)
Article
Chemistry, Multidisciplinary
Dmytro A. Ivashchenko, Nuno M. F. S. A. Cerqueira, Alexandre L. Magalhaes
Summary: Ion mobility-mass spectrometry, combined with a computational approach, has shown reliable identification of various compounds; while experimental advancements have been made, theoretical improvements have been slow; the work contributes to improving consistency between different theoretical results and between theoretical and experimental values.
STRUCTURAL CHEMISTRY
(2021)
Article
Chemistry, Physical
Alexandre V. Pinto, Pedro Ferreira, Rui P. P. Neves, Pedro A. Fernandes, Maria J. Ramos, Alexandre L. Magalhaes
Summary: This study analyzed the reaction mechanism of MHETase and found that it catalyzes the conversion of MHET in two steps, with a rate-limiting step activation barrier of 19.35 kcal/mol. The results supported the hypothesis that a transient tetrahedral intermediate mediates the reaction mechanism.
Article
Energy & Fuels
Harbil Bediaga, Maria Isabel Moreno, Sonia Arrasate, Jose Luis Vilas, Lucia Orbe, Elias Unzueta, Juan Perez Mercader, Humberto Gonzalez-Diaz
Summary: This study developed an IFPTML model for classifying gasoline samples using Information Fusion, Perturbation Theory, and Machine Learning algorithms, with over 230,000 outcomes from a petroleum refinery plant. The model showed high sensitivity and specificity on training and validation sets, as well as robustness to changes in experimental techniques.
Article
Chemistry, Multidisciplinary
Karel Dieguez-Santana, Bakhtiyor Rasulev, Humberto Gonzalez-Diaz
Summary: This paper introduces an application of information fusion perturbation-theory machine learning method in antibacterial drug-nanoparticle systems. The method accelerates the testing of bacterial sensitivity to different strains and shows good predictive performance. Additionally, the concept of MDR computational surveillance for detecting multidrug-resistant strains is introduced.
ENVIRONMENTAL SCIENCE-NANO
(2022)
Article
Environmental Sciences
Karel Dieguez-Santana, Manuel Mesias Nachimba-Mayanchi, Amilkar Puris, Roldan Torres Gutierrez, Humberto Gonzalez-Diaz
Summary: This study developed Quantitative Structure-Toxicity Relationship (QSTR) models using multiple statistical models and machine learning algorithms, and found that the Random Forest regression model was the most superior. The results suggest that the developed QSTR models can reliably predict pesticide toxicity in Americamysis bahia, and can be applied in pesticide screening and prioritization.
ENVIRONMENTAL RESEARCH
(2022)
Article
Chemistry, Medicinal
Carlos Santiago, Bernabe Ortega-Tenezaca, Iratxe Barbolla, Brenda Fundora-Ortiz, Sonia Arrasate, Maria Auxiliadora Dea-Ayuela, Humberto Gonzalez-Diaz, Nuria Sotomayor, Esther Lete
Summary: In this study, the authors used the SOFT.PTML tool to pre-process a ChEMBL dataset of pre-clinical assays of anti-leishmanial compound candidates. They compared different ML algorithms and found that the IFPTML-LOGR model had excellent specificity and sensitivity values. They illustrated the use of the software with a practical case study and identified compounds with potential activity. They also performed a computational high-throughput screening and validated the accuracy of the model.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Medicine, Research & Experimental
Karel Dieguez-Santana, Gerardo M. Casanola-Martin, Roldan Torres, Bakhtiyor Rasulev, James R. Green, Humbert Gonzalez-Diaz
Summary: This study utilized the IFPTML algorithm to analyze a large dataset from the ChEMBL database, investigating the interaction between antibacterial drugs and bacterial metabolic networks. The results showed that both linear and nonlinear models had good statistical parameters and were able to predict antibacterial compounds, potentially leading to the discovery of new metabolic mutations in antibiotic resistance.
MOLECULAR PHARMACEUTICS
(2022)
Article
Chemistry, Medicinal
Karel Dieguez-Santana, Amilkar Puris, Oscar M. Rivera-Borroto, Gerardo M. Casanola-Martin, Bakhtiyor Rasulev, Humberto Gonzalez-Diaz
Summary: This study proposes a new machine learning algorithm called FURIA-C for classifying drug-like compounds with antidiabetic inhibitory ability. The algorithm achieved satisfactory accuracy scores and derived fuzzy rules with high Certainty Factor values. Comparison tests showed that FURIA-C outperforms other methods, making it a cutting-edge technique for predicting the inhibitory activity of new compounds and speeding up the discovery of multi-target antidiabetic agents.
CURRENT COMPUTER-AIDED DRUG DESIGN
(2022)
Article
Chemistry, Physical
Alexandre V. Pinto, Pedro Ferreira, Pedro A. . Fernandes, Alexandre L. Magalhaes, Maria J. Ramos
Summary: In this paper, a new molecular dynamics (MD) model is proposed to accurately describe the structure of graphene oxide (GO) and its interaction with a solvent and other adsorbate molecules. The new force field parameters are derived through linear-scaling density functional theory calculations, which better reproduce the solvent structure observed in ab initio MD simulations. The effect of ionic strength and the carbon-to-oxygen ratio on the distribution of charges surrounding GO sheets is also analyzed, and the force field is validated by simulating the adsorption of natural amino acid molecules to GO sheets and estimating their free energy of binding.
JOURNAL OF PHYSICAL CHEMISTRY B
(2022)
Article
Chemistry, Applied
K. Biernacki, J. Lopes, R. Afonso, A. Mendes, L. Gales, A. L. Magalhaes
Summary: The study demonstrates that microporous crystals of L-Isoleucyl-L-Valine and L-Valyl-L-Isoleucine can effectively distinguish between propane and propylene mixtures. Despite having similar pore diameters, L-Valyl-L-Isoleucine shows higher separation selectivity.
MICROPOROUS AND MESOPOROUS MATERIALS
(2022)
Article
Biology
Karel Dieguez-Santana, Humberto Gonzalez-Diaz
Summary: This article utilizes machine learning methods to predict the activity of unknown drugs and discover potential antibacterial drugs. Through a bibliometric study of 1596 Scopus documents from 2006 to 2022, the contributions of leading authors, universities/organizations, and countries are analyzed in terms of productivity, citations, and bibliographic linkage. Essential topics related to the application of machine learning in antibacterial development are identified, and emerging topics are proposed. The applied methodology contributes to a broader and more specific understanding of machine learning research in antibacterial studies for future projects.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Multidisciplinary
Karel Dieguez-Santana, Humberto Gonzalez-Diaz
Summary: The study utilizes Artificial Intelligence and Machine Learning algorithms to accelerate the design of systems composed of antibacterial drugs and nanoparticles, analyzing a large dataset. Training alternative models with different algorithms, as well as studying simulated behavior of DADNPs in biological assays.
Article
Biology
Iain Hunter, Raz Leib
Summary: Natural movement is related to health, but it is difficult to measure. Existing methods cannot capture the full range of natural movement. Comparing movement across different species helps identify common biomechanical and computational principles. Developing a system to quantify movement in freely moving animals in natural environments and relating it to life quality is crucial. This study proposes a theoretical framework based on movement ability and validates it in Drosophila.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Andy Gardner
Summary: Fisher's geometric model is a useful tool for predicting key properties of Darwinian adaptation, and here it is applied to predict differences between the evolution of altruistic versus nonsocial phenotypes. The results suggest that the effect size maximizing probability of fixation is smaller in the context of altruism and larger in the context of nonsocial phenotypes, leading to lower overall probability of fixation for altruism and higher overall probability of fixation for nonsocial phenotypes.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Thomas F. Pak, Joe Pitt-Francis, Ruth E. Baker
Summary: Cell competition is a process where cells interact in multicellular organisms to determine a winner or loser status, with loser cells being eliminated through programmed cell death. The winner cells then populate the tissue. The outcome of cell competition is context-dependent, as the same cell type can win or lose depending on the competing cell type. This paper proposes a mathematical framework to study the emergence of winner or loser status, highlighting the role of active cell death and identifying the factors that drive cell competition in a cell-based modeling context.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Haruto Tomizuka, Yuuya Tachiki
Summary: Batesian mimicry is a strategy in which palatable prey species resemble unpalatable prey species to avoid predation. The evolution of this mimicry plays a crucial role in protecting the unpalatable species from extinction.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Jason W. Olejarz, Martin A. Nowak
Summary: Gene drive technology shows potential for population control, but its release may have unpredictable consequences. The study suggests that the failure of suppression is a natural outcome, and there are complex dynamics among wild populations.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Hamid Ravaee, Mohammad Hossein Manshaei, Mehran Safayani, Javad Salimi Sartakhti
Summary: Gene expression analysis is valuable for cancer classification and phenotype identification. IP3G, based on Generative Adversarial Networks, enhances gene expression data and discovers phenotypes in an unsupervised manner. By converting gene expression profiles into images and utilizing IP3G, new phenotype profiles can be generated, improving classification accuracy.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Beatrix Rahnsch, Leila Taghizadeh
Summary: This study forecasts the evolution of the COVID-19 pandemic in Germany using a network-based inference method and compares it with other approaches. The results show that the network-inference based approach outperforms other methods in short-to mid-term predictions, even with limited information about the new disease. Furthermore, predictions based on the estimation of the reproduction number in Germany can yield more reliable results with increasing data availability, but still cannot surpass the network-inference based algorithm.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Rongsheng Huang, Qiaojun Situ, Jinzhi Lei
Summary: Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. Random inheritance of epigenetic states plays a pivotal role in stem cell differentiation. This computational model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming, offering a promising path for enhancing the field of regenerative medicine.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao
Summary: This study compares insulin signaling in healthy and type 2 diabetes states using reaction network analysis. The results show similarities and differences between the two conditions, providing insights into the mechanisms of insulin resistance, including the involvement of other complexes, less restrictive interplay between species, and loss of concentration robustness in GLUT4.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Nuverah Mohsin, Heiko Enderling, Renee Brady-Nicholls, Mohammad U. Zahid
Summary: Mathematical modeling is crucial in understanding radiobiology and designing treatment approaches in radiotherapy for cancer. This study compares three tumor volume dynamics models and analyzes the implications of model selection. A new metric, the point of maximum reduction of tumor volume (MRV), is introduced to quantify the impact of radiotherapy. The results emphasize the importance of caution in selecting models of response to radiotherapy due to the artifacts imposed by each model.
JOURNAL OF THEORETICAL BIOLOGY
(2024)
Article
Biology
Armindo Salvador
Summary: Michael Savageau's Biochemical Systems Analysis papers have had a significant impact on Systems Biology, generating core concepts and tools. This article provides a brief summary of these papers and discusses the most relevant developments in Biochemical Systems Theory since their publication.
JOURNAL OF THEORETICAL BIOLOGY
(2024)