In silico resources help combat cancer drug resistance mediated by target mutations.

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

[Spatial variation and optimization of ecological carrying capacity in Jiangsu coastal area, China]. / 江苏沿海地区生态承载力空间分异特征及优化.

The research on spatial pattern and optimization of regional ecological carrying capacity is especially necessary to promote the overall development of economic transformation, upgrading and ecological protection and restoration. This study chose the natural, social and economic complex ecosystem of Jiangsu coastal area as subject. The concept model of pressure-state-potential (PSP) of ecological carrying capacity was established. A total of 15 indices were used to represent the interaction of three level states. With the support of GIS and other space technologies, the magnitude, spatial variation of regional ecological carrying capacity were examined through comprehensive evaluation of multiple indices and analysis of single index. Results showed that the spatial pattern of the suitability of single factor ecological carrying capacity in Jiangsu coastal area showed a variety of trends. There were significant differences in the area, proportion and layout of the same index across different cities and counties. The spatial differentiation of pressure and state superposition index showed that low pressure suitable bearing, low pressure generally suitable bearing, low pressure unsuitable bearing, high pressure suitable bearing, high pressure generally suitable bearing, bearing high pressure unsuitable bearing area was 3971.42, 6885.21, 3705.23, 4597.03, 6853.26, and 6403.97 km2, accounting for 12.3%, 21.2%, 11.4%, 14.2%, 21.1% and 19.8% of the total area, respectively. The results of spatial differentiation of superposition index of state and potential showed that the area integral of low potential suitable bearing, low potential general sui-table bearing, low potential unsuitable bearing, high potential suitable bearing, high potential general sui-table bearing and high potential unsuitable bearing was 3030.79, 6545.30, 5874.31, 3253.31, 7614.38, and 6097.83 km2, accounting for 9.4%, 20.2%, 18.1%, 10.0%, 23.5% and 18.8% of the total area, respectively. Most areas showed distribution characteristics of low pressure bearing area corresponding to high potential bearing area and high pressure bearing area corresponding to low potential bearing area in spatial pattern. There was an interaction of mutual influence, mutual promotion and collaborative promotion between pressure and state superposition index and state and potential superposition index.