The surface stress of biomedical silicones is a stimulant of cellular response.

Silicones are commonly used for lubrication of syringes, encapsulation of medical devices, and fabrication of surgical implants. While silicones are generally viewed as relatively inert to the cellular milieu, they can mediate a variety of inflammatory responses and other deleterious effects, but the mechanisms underlying the bioactivity of silicones remain unresolved. Here, we report that silicone liquids and gels have high surface stresses that can strongly resist deformation at cellular length scales. Biomedical silicones, including syringe lubricants and fillings from FDA-approved breast implants, readily adsorb matrix proteins and activate canonical rigidity sensing pathways through their surface stresses. In 3D culture models, liquid silicone droplets support robust cellular adhesion and the formation of multinucleated monocyte-derived cell masses that recapitulate phenotypic aspects of granuloma formation in the foreign body response. Together, our findings implicate surface stress as a cellular stimulant that should be considered in application of silicones for biomedical purposes.

Highly selective inhibition of myosin motors provides the basis of potential therapeutic application.

Direct inhibition of smooth muscle myosin (SMM) is a potential means to treat hypercontractile smooth muscle diseases. The selective inhibitor CK-2018571 prevents strong binding to actin and promotes muscle relaxation in vitro and in vivo. The crystal structure of the SMM/drug complex reveals that CK-2018571 binds to a novel allosteric pocket that opens up during the "recovery stroke" transition necessary to reprime the motor. Trapped in an intermediate of this fast transition, SMM is inhibited with high selectivity compared with skeletal muscle myosin (IC50 = 9 nM and 11,300 nM, respectively), although all of the binding site residues are identical in these motors. This structure provides a starting point from which to design highly specific myosin modulators to treat several human diseases. It further illustrates the potential of targeting transition intermediates of molecular machines to develop exquisitely selective pharmacological agents.

Structure-based predictors of resistance to the HIV-1 integrase inhibitor Elvitegravir.

The enzyme integrase (IN) of human immunodeficiency virus type 1 (HIV-1) mediates integration of reverse transcribed viral DNA into the human genome, an essential step in the HIV-1 replication cycle. Elvitegravir (EVG) is an HIV-1 strand transfer inhibitor that binds IN and is the second drug in its class to be approved for clinical use in combination with other anti-HIV-1 medications. However, certain IN sequence mutational patterns have an effect on inhibitor binding, thereby altering the degree of IN mutant susceptibility to EVG. Employing a dataset of 115 translated IN sequences, each having a known EVG susceptibility value and consisting of a distinct set of amino acid replacements relative to the native IN, here we develop and evaluate statistical learning models for predicting the phenotypes (i.e., quantified EVG susceptibilities) of new IN mutants based solely on their genotypes (i.e., translated IN sequences). Each IN mutant is represented as a feature vector of structure-based attributes obtained via an in silico mutagenesis procedure that quantifies all anticipated IN residue-specific environmental perturbations from wild type upon mutation. Cross-validated performance based on four classification models show that balanced accuracy reaches 87%, while two regression models yield a Pearson's correlation coefficient as high as r=0.78. At the present time, the models may potentially be useful for diagnostic purposes, but only in conjunction with other tools and techniques, including experimental phenotype assays. However, as published experimental phenotypes for new IN variants become available, a larger and more diverse training set will likely lead to significantly more accurate models.