Retroviral protease (PR) from the human immunodeficiency virus type 1 (HIV-1) was identified over a decade ago as a potential target for structure-based drug design. This effort was very successful. Four drugs are already approved, and others are undergoing clinical trials. The techniques utilized in this remarkable example of structure-assisted drug design included crystallography, NMR, computational studies, and advanced chemical synthesis. The development of these drugs is discussed in detail. Other approaches to designing HIV-1 PR inhibitors, based on the concepts of symmetry and on the replacement of a water molecule that had been found tetrahedrally coordinated between the enzyme and the inhibitors, are also discussed. The emergence of drug-induced mutations of HIV-1 PR leads to rapid loss of potency of the existing drugs and to the need to continue the development process. The structural basis of drug resistance and the ways of overcoming this phenomenon are mentioned.

For a series of different proteins, including a structural protein, enzyme, inhibitor, protein marker, and a charge-transfer system, we have quantified the higher affinity of Na ؉ over K ؉ to the protein surface by means of molecular dynamics simulations and conductivity measurements. Both approaches show that sodium binds at least twice as strongly to the protein surface than potassium does with this effect being present in all proteins under study. Different parts of the protein exterior are responsible to a varying degree for the higher surface affinity of sodium, with the charged carboxylic groups of aspartate and glutamate playing the most important role. Therefore, local ion pairing is the key to the surface preference of sodium over potassium, which is further demonstrated and quantified by simulations of glutamate and aspartate in the form of isolated amino acids as well as short oligopeptides. As a matter of fact, the effect is already present at the level of preferential pairing of the smallest carboxylate anions, formate or acetate, with Na ؉ versus K ؉ , as shown by molecular dynamics and ab initio quantum chemical calculations. By quantifying and rationalizing the higher preference of sodium over potassium to protein surfaces, the present study opens a way to molecular understanding of many ion-specific (Hofmeister) phenomena involving protein interactions in salt solutions.ion-protein interaction ͉ molecular dynamics ͉ cell environment ͉ protein function ͉ Hofmeister series S odium and potassium represent the two most abundant monovalent cations in living organisms. Despite the fact that they possess the same charge and differ only in size [Na ϩ having a smaller ionic radius but a larger hydrated radius than K ϩ (1)], sodium versus potassium ion specificity plays a crucial role in many biochemical processes. More than 100 years ago, Hofmeister discovered that Na ϩ destabilizes (''salts out'') hens' egg white protein more efficiently than K ϩ does (2), analogous behavior being later shown also for other proteins (3). In a similar way, sodium was found to be significantly more efficient than potassium, e.g., in enhancing polymerization of rat brain tubulin (4). 23 Na NMR studies also were used to characterize cation-binding sites in proteins (5). The vital biological relevance of the difference between Na ϩ and K ϩ is exemplified by the low intracellular and high extracellular sodium͞potassium ratio maintained in living cells by ion pumps at a considerable energy cost (6). The principal goal of this article is to quantify and rationalize the different affinities of sodium and potassium to protein surfaces by means of molecular dynamics (MD) simulations, quantum chemistry calculations, and conductivity measurements for a series of proteins and protein fragments in aqueous solutions. Our effort, which is aimed at understanding the generic, thermodynamic Na ϩ ͞K ϩ ion specificity at protein surfaces, is thus complementary to recent computational studies of ion selectivity during active transport across ...

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.