1. Pseudolikelihood maximization Direct-Coupling Analysis (plmDCA) by Magnus Ekeberg

This web page contains MATLAB-code (and accompanying C-written routines) for plmDCA. plmDCA takes as input a Multiple Sequence Alignment and returns scores for pairwise (direct) interactions among the columns.

2. Fields of inference and optimization in networks group
The individual project descriptions have links to software codes used.

3. Improving contact prediction along three dimensions

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to

filter and align the raw sequence data representing the evolutionarily related proteins;
choose a predictive model to describe a sequence alignment;
infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map.

We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model.

4. Gaussian Direct Coupling Analysis for protein contacts prediction

 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

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