Improving landscape inference by integrating heterogeneous data in the inverse Ising problem

Abstract

The inverse Ising problem and its generalizations to Potts and continuous spin models have recently attracted much attention thanks to their successful applications in the statistical modeling of biological data. In the standard setting, the parameters of an Ising model (couplings and fields) are inferred using a sample of equilibrium configurations drawn from the Boltzmann distribution. However, in the context of biological applications, quantitative information for a limited number of microscopic spins configurations has recently become available. In this paper, we extend the usual setting of the inverse Ising model by developing an integrative approach combining the equilibrium sample with (possibly noisy) measurements of the energy performed for a number of arbitrary configurations. Using simulated data, we show that our integrative approach outperforms standard inference based only on the equilibrium sample or the energy measurements, including error correction of noisy energy measurements. As a biological proof-of-concept application, we show that mutational fitness landscapes in proteins can be better described when combining evolutionary sequence data with complementary structural information about mutant sequences.

arXiv commentAccepted for publication in Scientific Reports. 11 pages, 4 figures

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Cited by in corpus (5)

• Inverse Statistical Physics of Protein Sequences: A Key Issues Review
• Emergent time scales of epistasis in protein evolution
• Assessing the accuracy of direct-coupling analysis for RNA contact prediction
• Direct Coupling Analysis of Epistasis in Allosteric Materials
• Optimal design of experiments by combining coarse and fine measurements