Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Introduction Congenital fibrinogen disorders (CFDs) comprise the quantitative and qualitative fibrinogen molecule abnormalities that are caused by fibrinogen gene mutations. The objective of this cohort research was to study the molecular and clinical profiles of patients with CFDs. Materials and methods Genomic DNA Sanger sequencing of 14 Iranian patients was performed to determine CFDs‐causing mutations. The disorders were diagnosed by routine and specific (fibrinogen antigen and functional assay) coagulation tests, and clinical data were obtained from medical records. Molecular dynamics (MD) simulations were performed to investigate the effect of missense mutation on the protein structure. Results Thirteen out of 14 patients had afibrinogenemia while the remaining patient had dysfibrinogenemia. Umbilical cord bleeding was the most common clinical presentation (n: 9, ~70%) which led to the diagnosis of afibrinogenemia, while menorrhagia led to the diagnosis of dysfibrinogenemia. Six homozygous mutations were identified in afibrinogenemia: three previously described variants in FGA (p.Trp52Ter, p.Ser312AlafsTer109 and p.Gly316GlufsTer105), one in FBG (p.Gly430Asp), and two novel mutations in FGB (p.Gly430Arg) and FGG (p.His366ThrfsTer40), while the FGA (p.Arg38Thr) heterozygous mutation was identified in dysfibrinogenemia. MD simulation indicated that the FGA p. Arg38Thr mutation probably interferes with polymerization of fibrin monomers. Conclusions In Iran, with its high rate of consanguinity, autosomal recessive afibrinogenemia with severe clinical presentations is relatively common due to heterogeneous molecular defects.

Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.

Inhibition protein-protein interactions (PPIs) using small molecules, that interfere with the formation of these complexes, modulates critical regulatory pathways and has therapeutic significance. DBF4-dependent kinase CDC7 is the S-phase checkpoint pathway target, which plays an important role for a proper response to DNA damage and replicative stress in multiple organisms. Overexpression of CDC7 and its protein regulator DBF4 is highly neurotoxic and promotes cancer and neurodegeneration. In the present study, virtual screening of inhibitor scaffolds mimicking DBF4 pharmacophoric properties was carried out and evaluation of their potential inhibitory activity toward CDC7 was performed using high-throughput docking and molecular dynamics simulations. The calculations identified five small molecules exhibiting a high affinity to the active site region of the CDC7 protein.

The identification of the critical nodes and optimal path mediating the dynamical network communication could offer new strategies to manipulate TDP-43 function. Disrupting a specific network communication could represent a rational approach to the design of drugs with improved potency and selectivity.