Intra- and Inter-Molecular Coevolution: The Case of HIV1 Protease and Reverse Transcriptase

Boba, Patrick; Weil, Philipp; Hoffgaard, Franziska; Hamacher, Kay

doi:10.1007/978-3-642-18472-7_28

Cited by 5 publications

(7 citation statements)

References 21 publications

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“…In molecular evolution, the understanding of the biophysical ramifications of evolutionary "hot spots" in the molecule helps to analyze the selective pressure the molecular phenotype is subject to [18,39].…”

Section: Discussionmentioning

confidence: 99%

Free energy of contact formation in proteins: Efficient computation in the elastic network approximation

Hamacher

2011

Phys. Rev. E

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Biomolecular simulations have become a major tool in understanding biomolecules and their complexes. However, one can typically only investigate a few mutants or scenarios due to the severe computational demands of such simulations, leading to a great interest in method development to overcome this restriction. One way to achieve this is to reduce the complexity of the systems by an approximation of the forces acting upon the constituents of the molecule. The harmonic approximation used in elastic network models simplifies the physical complexity to the most reduced dynamics of these molecular systems. The reduced polymer modeled this way is typically comprised of mass points representing coarse-grained versions of, e.g., amino acids. In this work, we show how the computation of free energy contributions of contacts between two residues within the molecule can be reduced to a simple lookup operation in a precomputable matrix. Being able to compute such contributions is of great importance: protein design or molecular evolution changes introduce perturbations to these pair interactions, so we need to understand their impact. Perturbation to the interactions occurs due to randomized and fixated changes (in molecular evolution) or designed modifications of the protein structures (in bioengineering). These perturbations are modifications in the topology and the strength of the interactions modeled by the elastic network models. We apply the new algorithm to (1) the bovine trypsin inhibitor, a well-known enzyme in biomedicine, and show the connection to folding properties and the hydrophobic collapse hypothesis and (2) the serine proteinase inhibitor CI-2 and show the correlation to Φ values to characterize folding importance. Furthermore, we discuss the computational complexity and show empirical results for the average case, sampled over a library of 77 structurally diverse proteins. We found a relative speedup of up to 10 000-fold for large proteins with respect to repeated application of the initial model.

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Section: Discussionmentioning

confidence: 99%

Free energy of contact formation in proteins: Efficient computation in the elastic network approximation

Hamacher

2011

Phys. Rev. E

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“…We used the method of subset MI which calculates the frequencies of amino acid occurrence p (σ i ) in a column i differently depending on where the current partner column j possesses gaps 9. Only those rows that show no gaps in i and j are considered in the computation of p (σ i ), p (σ j ), and p (σ i ,σ j ).…”

Section: Methodsmentioning

confidence: 99%

“…We chose to apply the nonparametric method of MI since parametric and phylogeny‐based methods are subject to statistical noise due to their dependence on preliminary assumptions. MI, on the other hand, has proven to be more resilient in this aspect 7–10. However, one should keep in mind that MI is an information‐theoretical measure which is based on the alphabet of amino acid symbols while physical and chemical characteristics of the residues are neglected.…”

Section: Introductionmentioning

confidence: 99%

Structure‐based, biophysical annotation of molecular coevolution of acetylcholinesterase

2011

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Acetylcholinesterase (AChE) is an important enzyme in the nervous system. It terminates signal transmission at chemical synapses by degrading the neurotransmitter acetylcholine and was found to play a role in plaque formation in Alzheimer's disease. Several functional parts of its structure have been identified in the past. Here, we use a coarse-grained anisotropic network model approach based on structure data to analyze protein mechanics of AChE. Single contacts in the protein are "switched off" and the change in the intrinsic dynamics is measured. We correlate the gained insight with information about coevolution within the molecule derived from multiple sequence alignments. More than 300 AChE sequences were aligned and the mutual information of the positions was calculated. From these structural, biophysical, and evolutionary data we could reveal sites of coevolutionary signatures in AChE, annotate them by the selective pressure induced for biophysical reasons, and further pave the way for a more detailed understanding of evolutionary boundary conditions for AChE.

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“…The shuffling destroys residual correlations, expressed in the Hij and H ijk between the MSA columns, while maintaining the individual column entropies Hi, Hj, and H k . Assessing the "background noise" and finite-size effects in real-world data [3] suggested that the scaling law of this statistical effect is…”

Section: Null-modelmentioning

confidence: 99%

Information-theoretic analysis of molecular (co)evolution using graphics processing units

Waechter

Jaeger

Weißgraeber

et al. 2012

Proceedings of the 3rd International Workshop on Emerging Computational Methods for the Life Sciences

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We present a massively-parallel implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two-and three-point correlations within any multiple-sequence alignment. The high computational demand in the normalization procedure is efficiently met by an implementation on Graphics Processing Units (GPUs) using NVIDIA's CUDA framework. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods [11] but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, that can only be achieved using GPUs. We illustrate a) the computational efficiency and b) the biological usefulness of two-and three-point MI by an application to the well-known protein calmodulin. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of this molecule.

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Intra- and Inter-Molecular Coevolution: The Case of HIV1 Protease and Reverse Transcriptase

Cited by 5 publications

References 21 publications

Free energy of contact formation in proteins: Efficient computation in the elastic network approximation

Free energy of contact formation in proteins: Efficient computation in the elastic network approximation

Structure‐based, biophysical annotation of molecular coevolution of acetylcholinesterase

Information-theoretic analysis of molecular (co)evolution using graphics processing units

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