Value of predictive bioinformatics in inherited metabolic diseases

Typically,inherited metabolic diseases arise from point mutations in genes encoding metabolic enzymes. Although some of these mutations directly affect amino acid residues in the active sites of these enzymes,the majority do not. It is now well accepted that the majority of these disease-associated mutations exert their effects through alteration of protein stability,which causes a reduction in enzymatic activity. This finding suggests a way to predict the severity of newly discovered mutations. In silico prediction of the effects of amino acid sequence alterations on protein stability often correlates with disease severity. However,no stability prediction tool is perfect and,in general,better results are obtained if the predictions from a variety of tools are combined and then interpreted. In addition to predicted alterations to stability,the degree of conservation of a particular residue can also be a factor which needs to be taken into account: alterations to highly conserved residues are more likely to be associated with severe forms of the disease. The approach has been successfully applied in a variety of inherited metabolic diseases,but further improvements are necessary to enable robust translation into clinically useful tools.

New techniques and strategies in drug discovery

Great success has been witnessed in last decades,some new techniques and strategies have been widely used in drug discovery.In this roadmap,several representative techniques and strategies are highlighted to show recent advances in this filed.(A)A DOX protocol has been developed for accurate protein-ligand binding structure prediction,in which first principle method was used to rank the binding poses.Validation against crystal structures have found that DOX prediction achieved an impressive success rate of 99%,indicating significant improvement over molecular docking method.(B)Virtual target profiling is a compound-centric strategy enabling a parallel implementation of interrogating compounds against various targets in a single screen,which has been used in hit/lead identification,drug repositioning,and mechanism-of-action studies.Current and emerging methods for virtual target profiling are briefly summarized herein.(C)Research on targeted autophagy to treat diseases has received encouraging progress.However,due to the complexity of autophagy and disease,experimental and in silico methods should be performed synergistically for the entire process.This part focuses on in silico methods in autophagy research to promote their use in medicinal research.(D)Histone deacetylases(HDACs)play important roles in various biological functions through the deacetylation of lysine residues.Recent studies demonstrated that HDACs,which possess low deacetylase activities,exhibited more efficient defatty-acylase activities.Here,we review the defatty-acylase activity of HDACs and describe examples for the design of isoform selective HDAC inhibitor.(E)The FDA approval of three kinase allosteric inhibitors and some others entering clinical study has spurred considerable interests in this targeted drug discovery area.(F)Recent advances are reviewed in structure-based design of novel antiviral agents to combat drug resistance.(G)Since nitric oxide(NO)exerts anticancer activity depending on its concentration,optimal levels of NO in cancer cells is desirable.In this minireview,we briefly describe recent advances in the research of NO-based anticancer agents by our group and present some opinions on the future development of these agents.(H)The field of photoactivation strategies have been extensively developed for controlling chemical and biological processes with light.This review will summarize and provide insight into recent research advances in the understanding of photoactivatable molecules including photoactivatable caged prodrugs and photoswitchable molecules.