20,257 research outputs found
Developing Predictive Molecular Maps of Human Disease through Community-based Modeling
The failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding suitable
choices of methods for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate, unbiased, and
informative results. Here, we summarize key practical guidelines and
recommendations for performing high-quality benchmarking analyses, based on our
experiences in computational biology.Comment: Minor update
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology
Cyclin-dependent kinases as drug targets for cell growth and proliferation disorders. A role for systems biology approach in drug development. Part II - CDKs as drug targets in hypertrophic cell growth. Modelling of drugs targeting CDKs
Cyclin-dependent kinases (CDKs) are key regulators of cell growth and proliferation. Impaired regulation of their activity leads to various diseases such as cancer and heart hypertrophy. Consequently, a number of CDKs are considered as targets for drug discovery. We review the development of inhibitors of CDK2 as anti-cancer drugs in the first part of the paper and in the second part, respectively, the development of inhibitors of CDK9 as potential therapeutics for heart hypertrophy. We argue that the above diseases are systems biology, or network diseases. In order to fully understand the complexity of the cell growth and proliferation disorders, in addition to experimental sciences, a systems biology approach, involving mathematical and computational modelling ought to be employed
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