biocomp

Computational Biology

GaussDCA: Multivariate Gaussian Inference of Protein Contacts from Multiple Sequence Alignment

AttachmentSize
GaussDCA-julia.tgz25.09 KB
GaussDCA-matlab.tgz24.36 KB

This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners" by Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721

The code comes in two versions, one for Julia and one for MATLAB. They provide nearly identical funcitionality. The Julia code is slightly faster, and doesn't require compilation of external modules.

Julia code

You can download the Julia code from the attached file "GaussDCA-julia.tgz"; however, the recommended way to obtain the code is by using the command Pkg.clone("https://github.com/carlobaldassi/GaussDCA.jl") in the julia command line. See also the documentation at https://github.com/carlobaldassi/GaussDCA.jl.

Matlab code

You can download the MATLAB code from the attached file "GaussDCA-matlab.tgz", or from https://github.com/carlobaldassi/GaussDCA.matlab. See the README.md file for instructions.

Protein contact prediction: prospects and reality

Recent developments in co-evolution based contact prediction have been recently widely adopted by the structure prediction community, as evidenced in CASP11, a recently finished commnity-wide experiment in blind protein structure prediction.. According to organisers’ opinion, contact prediction was one of the “winners” of CASP this year. In this talk I will present how did the field of contact prediction progress recently and how are the inferred couplings applied to solving problems in biological settings. This talk will particularly focus on my contact-driven CASP method “my protein&me”, which ranked as 4th most group world-wide and was claimed as one of more unexpected developments in this years’ experiment. I will also discuss several success stories for contact prediction, in which successful contact inference allowed for discovering structural information that has not been attainable by other means. Finally, the talk will discuss the potential impact of introducing additional biological information in the inference process and prospective ways of increasing the applicability of these methods.

Date: 
Thu, 15/01/2015 - 14:30
Speaker: 
Marcin J. Skwark (The Finnish Centre of Excellence in Computational Inference)
Place: 
HUGEF, old building 1st floor "Aula Affrescata"

Engineering bacterial strains for cellulosic biorefinery

Cellulose waste biomass is the most abundant and attractive substrate for biorefineries aimed at producing industrially relevant compounds (e.g. fuels, plastics, building blocks) by economically and environmentally sustainable fermentation processes. However, cellulose is highly recalcitrant to biodegradation and its conversion by biotechnological strategies currently requires very expensive multistep processes. Notably, the need for dedicated cellulase production still is major constraint to cost-effective bioconversion of cellulosic biomass.

Extensive effort has been produced by research groups worlwide aimed at developing recombinant microorganisms able to perform single step cellulose fermentation (i.e., consolidated bioprocessing, CBP) to high-value chemicals. Two main paradigms have been applied so far: a) “native cellulolytic strategies”, aimed at conferring high-value product properties to natural cellulolytic microorganisms; b) “recombinant cellulolytic strategies”, aimed to confer cellulolytic ability to microorganisms exhibiting high product yields and titers.

Basic knowledge about native biochemical systems enabling depolymerization and metabolism of cellulose, metabolic pathways for producing some of the most requested chemicals (e.g., liquid fuels), and fundamentals and examples of native and recombinant cellulolytic strategies will be illustrated.

Date: 
Thu, 20/11/2014 - 15:30
Speaker: 
Roberto Mazzoli, Dept. of Life Sciences and Systems Biology, University of Torino
Place: 
Aula Seminari Cortile MBC

Elucidating bacterial growth laws in silico

Proteome organization in bacteria is actively regulated in response to the growth conditions. In specific, as their growth rate changes, bacteria adjust the relative amounts of ribosomal, transport and reaction-catalyzing proteins in a robust, highly reproducible manner. Several phenomenological models provide a qualitative explanation for these facts. By contrast, genome-scale approaches probing such relationships are far less developed. In this talk a constraint-based metabolic modeling scheme called Constrained Allocation Flux Balance Analysis (CAFBA) will be presented, that accounts effectively for the costs of protein expression. By tuning a very small number of adjustable parameters, CAFBA is able to reproduce the empirical growth laws (including the elusive `overflow metabolism') with a remarkable degree of accuracy, generating along the way a variety of testable predictions ranging from the usage of pathways to protein expression levels. Analysis of CAFBA solutions furthermore sheds new light on the cross-over from oxidative to fermentative energetics that occurs in many bacteria as the growth rate increases. Applicability of CAFBA's framework to other, potentially more interesting cell types will also be discussed.

Date: 
Mon, 24/11/2014 - 11:00
Speaker: 
Andrea De Martino
Place: 
HuGeF, Via Nizza 52, 1st floor, old building.

Signal localization as a phase separation process

It is well known that ultrasensitivity (Goldbeter & Koshland, 1981) is the core of many bistable switches in biological systems. It is not as well recognized that when ultrasensitive self-amplifying circuits are diffusively coupled in a spatially distributed system such as the cell plasmamembrane, they may induce its dynamic separation into distinct signaling phases. This basic mechanism lays behind the process of cell membrane polarization in many, diverse biological systems. Cell membrane polarization is implicated in basic biological phenomena such as differentiation, proliferation, migration and morphogenesis of unicellular and multicellular organisms. Physical models based on the coupling of membrane diffusion with bistable enzymatic dynamics can reproduce a broad range of symmetry-breaking events, such as those observed in eukaryotic directional sensing, the apico-basal polarization of epithelial cells, the polarization of budding and mating yeast.

Date: 
Thu, 16/01/2014 - 14:30
Speaker: 
Andrea Gamba
Syndicate content