Free energy prediction of biomolecular processes using high speed robotics algorithms and its use in in-silico driven experimental research

Free energy prediction of biomolecular processes using high speed robotics algorithms and its use in in-silico driven experimental research 2018-2020
Acronym: ROBOFEP
Project director: Laurentiu Spiridon
External website: full link

By developing new methods which leverage high-speed robotics algorithms, we undertake the effort to enhance free energy prediction for biomolecular processes. Our focus includes integrating these advancements into existing APIs and applying them to key areas of molecular biosciences such as plant-pathogen interaction, drug design, and immunotherapy.

Critical quantities in experimental biomolecular research such as affinity constants, IC50s or rate constants are a direct reflection of the free energy in their associated processes. Therefore, in silico prediction of free energy can greatly enhance research effectiveness both in terms of costs and system detailed understanding. However, to predict in silico the free energy of a bio-molecular process is at the time being overwhelmingly difficult, especially when large biomolecules such as proteins or nucleic acids are involved - as a huge number of their configurations needs to be drawn from their practically infinite statistical thermodynamic ensemble.

To address these difficulties, we aim in this project to improve, adapt and use high speed robotics algorithms for molecular sampling that can easily escape the spatial and temporal scales in which classical methods are usually trapped. This project builds further upon a Hamiltonian Monte Carlo algorithm that we have recently developed to reproduce the Boltzmann distribution necessary for correct free energy predictions. Herein we plan to develop two new multiscaling methods, namely Generalized Adaptive Mixing (GAM) and Dynamic Adaptive Constraing (DAC), that can hike between scales without losing any level of force field detail or statistical mechanics rigor. These new methods can be a major leap in molecular simulation strategies.

We also aim by this project to implement these in the already existing Application Programming Interfaces (APIs) Simbody and Molmodel – for which we collaborate with groups from Stanford and Upsalla universities.

Last but not least we aim herein to use our methods to assist experimental work performed either by members of our team or in cooperation with various partners in the following hot topics in molecular biosciences: plant-pathogen interaction, HIV and HCV drug design and immunotherapy.

Laurentiu Spiridon, Dr.
Laurentiu Spiridon, Dr.

Laurentiu Spiridon is a researcher in the Institute of Biochemistry of the Romanian Academy. Laurentiu is currently working in Bioinformatics & Structural Biochemistry in the Bioinformatics & Structural Biochemistry.

The aim of the project is to improve, adapt and use high speed robotics algorithms for molecular sampling and may be considered as consisting of three main objectives:

- Objective a: Method Development which comprise: (a1) Develop and asses Generalized Adaptive Mixing (GAM) schemes to be used with MGCHMC; (a2) Develop Lennard-Jones soft-core potential within the context of generalized coordinates and asses its energy barriers crossing efficiency. (a3) Develop Dynamic Adaptive Constraining (DAC) during molecular simulation.

- Objective b: Software Development which comprise:

(b1) Develop and implement prerequisite software for (b2)-(b4); (b2) Implement Adaptive Mixing in MGCHMC as a function of the previous visited subspace. (b3) Implement Lennard-Jones soft-core potential. (b4) Implement Dynamic Adaptive Constraining in GCHMC to complement sub-objective (a3).

- Objective c: In Silico Assistance of Experiments which consists of:

(c1) Compute the constrained potential of mean force (cPMF) of flexible regions resulted from structure assessment of RGA4/5 CC domains homologs in Oryza, Brachypodium, Setaria, Sorghum, Hordeum sp. (c2.1) Sample the conformational space of the pre-cleavage model structure of HCV-NS2 using MGCHMC and use it to point the most likely binding pockets. (c2.2) HIV Integrase/ RAG1 - DNA complex simulation with MGCHMC to identify the most likely binding pockets; (c3.1) Calculate Binding Free Energies of oxidated/reduced forms of tyrosinase YMD peptide; (c3.2) Implement pseudo-SRM method on IBAR MS platform for quantification of tyrosinase YMD peptide from HLA-A*0201 positive melanoma cell panel.

Laurentiu Spiridon – Principal Investigator

Eliza-Cristina martin – PhD Student

Cristian Munteanu – Scientific Researcher

Gabriela Chiritoiu – Scientific Researcher

Marioara Chiritoiu-Butnaru – Scientific Researcher III

Martin EC, Sukarta OCA, Spiridon L, Grigore LG, Constantinescu V, Tacutu R, Goverse A, Petrescu AJ. LRRpredictor-A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers. Genes (Basel). 2020 Mar 8;11(3):286. doi: 10.3390/genes11030286. PMID: 32182725; PMCID: PMC7140858.

IF: 3.5 AIS:0.9


Spiridon L, Şulea TA, Minh DDL, Petrescu AJ. Robosample: A rigid-body molecular simulation program based on robot mechanics. Biochim Biophys Acta Gen Subj. 2020 Aug;1864(8):129616. doi: 10.1016/j.bbagen.2020.129616. Epub 2020 Apr 13. PMID: 32298789.

IF: 4.12 AIS:1.8


Wróblewski T, Spiridon L, Martin EC, Petrescu AJ, Cavanaugh K, Truco MJ, Xu H, Gozdowski D, Pawłowski K, Michelmore RW, Takken FLW. Genome-wide functional analyses of plant coiled-coil NLR-type pathogen receptors reveal essential roles of their N-terminal domain in oligomerization, networking, and immunity. PLoS Biol. 2018 Dec 12;16(12):e2005821. doi: 10.1371/journal.pbio.2005821. PMID: 30540748; PMCID: PMC6312357.

IF: 9.59 AIS: 5.6