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Manuel N. Melo Lab

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The Multiscale Modeling Lab employs computational molecular simulation models at different resolution scales to tackle a wide range of biological questions.

Manuel N. Melo
Investigador Auxiliar
PhD in 2010,Universidade de Lisboa

Phone (+351) 214469616 | Extension 1616
Email m.n.melo@itqb.unl.pt

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Research Interests

The Multiscale Modeling Lab employs computational molecular simulation models at different resolution scales to tackle a wide range of biological questions. The main research focus is the behavior of biological membranes, of proteins, and of membrane-embedded proteins. Applications range from the prediction of biophysical properties of membranes of different composition, to the study of protein association and aggregation, to the functional characterization of large protein-membrane assemblies.

The time and length scales that govern these systems can be of the order of tens of nanometers, and hundreds of microseconds. To be tractable, molecular-level simulations of such magnitude require the use of simplified models: we employ and co-develop the Martini coarse-grain model, in which individual chemical groups are simplified as being single "super" atoms. This reduction in system complexity can yield a performance speed-up close to 1000x, as compared to models with full atomistic detail.

 

Group Members

Martín Calvelo

Martín is a postdoctoral researcher studying protein aggregation in the context of neurodegenerative diseases.

Fernando Nunes

Fernando is a PhD student in collaboration with the Cristina Silva Pereira Lab, working on computational models of suberin.

Tâmela Madaloz

Tâmela is a visiting PhD student from Universidade Federal de Santa Catarina, working on the modeling of steroid hormones.

Ana Carolina Araújo

Ana Carolina is a Master's student focusing on the formation of pores in membranes.

João Catarino

João is a Master's student developing models of the bacterial cell wall.

 

Selected Publications

  1. Martini 3 Coarse-Grained Force Field for Cholesterol
    Luís Borges-Araújo, Ana C. Borges-Araújo, Tugba Nur Ozturk, Daniel P. Ramirez-Echemendia, Balázs Fábián, Timothy S. Carpenter, Sebastian Thallmair, Jonathan Barnoud, Helgi I. Ingólfsson, Gerhard Hummer, D. Peter Tieleman, Siewert J. Marrink, Paulo C. T. Souza, and Manuel N. Melo
    Journal of Chemical Theory and Computation, 2023, 19, 20, 7387–7404

  2. Improved Parameterization of Phosphatidylinositide Lipid Headgroups for the Martini 3 Coarse-Grain Force Field
    Luís Borges-Araújo, Paulo C. T. Souza, Fábio Fernandes, and Manuel N. Melo
    Journal of Chemical Theory and Computation, 2022, 18, 1, 357–373

  3. Localization Preference of Antimicrobial Peptides on Liquid-Disordered Membrane Domains
    Juanjuan Su, Siewert J. Marrink, Manuel N. Melo
    Frontiers in Cell and Developmental Biology, 2020, 8:350

  4. Coarse-Grained Parameterization of Nucleotide Cofactors and Metabolites: Protonation Constants, Partition Coefficients, and Model Topologies
    Filipe M. Sousa, Luís M. P. Lima, Clément Arnarez, Manuela M. Pereira, and Manuel N. Melo
    Journal of Chemical Information and Modeling, 2021, 61, 1, 335–346

 

Laboratory's Website

For further information please visit the laboratory's website

 

Modelação Multiescala (PT)

O Laboratório de Modelação Multiescala usa modelos computacionais com vários níveis de detalhe, para dar resposta a uma larga gama de perguntas biológicas. Focamo-nos no estudo de membranas biológicas, de proteínas, e de proteínas membranares, e pretendemos dar resposta a perguntas como De que modo dependem as propriedades de uma membrana da sua constituição? Como interatuam determinadas proteínas umas com as outras e como podemos controlar esse processo? Quais as características que permitem o funcionamento eficiente de grandes complexos proteína-membrana?

Em concreto, são usados modelos coarse-grain — nos quais cada grupo químico é considerado, simplificadamente, como uma única partícula — para se conseguir simular os grandes tamanhos e durações dos processos em estudo. Esta simplificação consegue acelerar as simulações por um fator de até 1000x, permitindo estudar sistemas muito maiores, e por muito mais tempo.

 

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