Temperature dependence of fluctuations in HIV1-protease

Drug-resistant HIV-1 protease regains functional dynamics through cleavage site coevolution

Drug resistance is caused by mutations that change the balance of recognition favoring substrate cleavage over inhibitor binding. Here, a structural dynamics perspective of the regained wild-type functioning in mutant HIV-1 proteases with coevolution of the natural substrates is provided. The collective dynamics of mutant structures of the protease bound to p1-p6 and NC-p1 substrates are assessed using the Anisotropic Network Model (ANM). The drug-induced protease mutations perturb the mechanistically crucial hinge axes that involve key sites for substrate binding and dimerization and mainly coordinate the intrinsic dynamics. Yet with substrate coevolution, while the wild-type dynamic behavior is restored in both p1-p6 ((LP) (1'F)p1-p6D30N/N88D) and NC-p1 ((AP) (2) (V)NC-p1V82A) bound proteases, the dynamic behavior of the NC-p1 bound protease variants (NC-p1V82A and (AP) (2) (V)NC-p1V82A) rather resemble those of the proteases bound to the other substrates, which is consistent with experimental studies. The orientational variations of residue fluctuations along the hinge axes in mutant structures justify the existence of coevolution in p1-p6 and NC-p1 substrates, that is, the dynamic behavior of hinge residues should contribute to the interdependent nature of substrate recognition. Overall, this study aids in the understanding of the structural dynamics basis of drug resistance and evolutionary optimization in the HIV-1 protease system.

Efficient quantification of the importance of contacts for the dynamical stability of proteins

Understanding the stability of the native state and the dynamics of a protein is of great importance for all areas of biomolecular design. The efficient estimation of the influence of individual contacts between amino acids in a protein structure is a first step in the reengineering of a particular protein for technological or pharmacological purposes. At the same time, the functional annotation of molecular evolution can be facilitated by such insight. Here, we use a recently suggested, information theoretical measure in biomolecular design - the Kullback-Leibler-divergence - to quantify and therefore rank residue-residue contacts within proteins according to their overall contribution to the molecular mechanics. We implement this protocol on the basis of a reduced molecular model, which allows us to use a well-known lemma of linear algebra to speed up the computation. The increase in computational performance is around 10(1)- to 10(4)-fold. We applied the method to two proteins to illustrate the protocol and its results. We found that our method can reliably identify key residues in the molecular mechanics and the protein fold in comparison to well-known properties in the serine protease inhibitor. We found significant correlations to experimental results, e.g., dissociation constants and Φ values.

Rationale for more diverse inhibitors in competition with substrates in HIV-1 protease

The structural fluctuations of HIV-1 protease in interaction with its substrates versus inhibitors were analyzed using the anisotropic network model. The directions of fluctuations in the most cooperative functional modes differ mainly around the dynamically key regions, i.e., the hinge axes, which appear to be more flexible in substrate complexes. The flexibility of HIV-1 protease is likely optimized for the substrates' turnover, resulting in substrate complexes being dynamic. In contrast, in an inhibitor complex, the inhibitor should bind and lock down to inactivate the active site. Protease and ligands are not independent. Substrates are also more flexible than inhibitors and have the potential to meet the dynamic distributions that are inherent in the protease. This may suggest a rationale and guidelines for designing inhibitors that can better fit the ensemble of binding sites that are dynamically accessible to the protease.

BioPhysConnectoR: Connecting sequence information and biophysical models

Background

One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s).

A complete, theoretical picture of the selective advantage, and thus a functional annotation, of (co-)mutations is still lacking. Using sequence-based and information theoretical inspired methods we can identify coevolving residues in proteins without understanding the underlying biophysical properties giving rise to such coevolutionary dynamics. Detailed (atomistic) simulations are prohibitively expensive. At the same time reduced molecular models are an efficient way to determine the reduced dynamics around the native state. The combination of sequence based approaches with such reduced models is therefore a promising approach to annotate evolutionary sequence changes.

Results

With the R package BioPhysConnectoR we provide a framework to connect the information theoretical domain of biomolecular sequences to biophysical properties of the encoded molecules - derived from reduced molecular models. To this end we have integrated several fragmented ideas into one single package ready to be used in connection with additional statistical routines in R. Additionally, the package leverages the power of modern multi-core architectures to reduce turn-around times in evolutionary and biomolecular design studies. Our package is a first step to achieve the above mentioned annotation of coevolution by reduced dynamics around the native state of proteins.

Conclusions

BioPhysConnectoR is implemented as an R package and distributed under GPL 2 license. It allows for efficient and perfectly parallelized functional annotation of coevolution found at the sequence level.