Mutation Sensitivity Maps: Identifying Residue Substitutions That Impact Protein Structure Via a Rigidity Analysis In Silico Mutation Approach

In silico target specific design of potential quinazoline-based anti-NSCLC agents

Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. In spite of great advances, treatment of the disease is a medical challenge. Epidermal-growth factor receptor (EGFR) has been taken as a promising cell surface target to develop anti-NSCLC therapies. The main bottleneck to attain clinical efficacy with current EGFR tyrosine kinase inhibitors (EGFR-TKIs) is the rapid spread of oncogenic mutations. Numerous efforts have been made for the synthesis of diverse EGFR-TKIs against resistance-conferring mutations. One of the best strategies to design potent agents would be to explore existing anti-NSCLC drugs at the nonclinical development stage and prioritize privileged structural patterns. Within current study, conformational stability of clinically frequent EGFR mutants (G719S, T790M, L858R and a double mutant form L858R/T790M) were validated via DynaMut and missense3D computational servers. Subsequently, structure activity relationship (SAR) and scaffold similarity inquiry were used to rationally propose a few erlotinib analogues. Intended molecules were subjected to molecular docking and top-scored binders were further analyzed through 50-ns all atom molecular dynamics (MD) simulations to infer the dynamic behavior. The aim was to offer potential binders to overwhelm clinically frequent EGFR-TK mutants. The linear interaction energy (LIE) method was applied to compute the binding free energies between EGFR and intended ligands. For this purpose, MD-based conformational sampling of ligand-enzyme complexes and ligand-water associations were used to acquire thermodynamic energy averages. Though mechanistic details are to be explored, results of the current study identify synthetically accessible quinazoline small molecules with potential affinity toward frequent EGFR-TK mutants.[Figure: see text]Communicated by Ramaswamy H. Sarma.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

PETRA: Drug Engineering via Rigidity Analysis

Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of new medicines with high efficacies. Nonetheless, drug development remains a time and cost intensive process. In this work, we have developed a computational pipeline for assessing how individual atoms contribute to a ligand’s effect on the structural stability of a biological target. Our approach takes as input a protein-ligand resolved PDB structure file and systematically generates all possible ligand variants. We assess how the atomic-level edits to the ligand alter the drug’s effect via a graph theoretic rigidity analysis approach. We demonstrate, via four case studies of common drugs, the utility of our pipeline and corroborate our analyses with known biophysical properties of the medicines, as reported in the literature.

Protein interaction disruption in cancer

Background

Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the disruption of specific interactions in cancer patients using somatic mutation data and protein interaction networks.

Methods

We extend standard network smoothing techniques to assign scores to the edges in a protein interaction network in addition to nodes. We use somatic mutations as input to our modified network smoothing method, producing scores that quantify the proximity of each edge to somatic mutations in individual samples.

Results

Using breast cancer mutation data, we show that predicted edges are significantly associated with patient survival and known ligand binding site mutations. In-silico analysis of protein binding further supports the ability of the method to infer novel disrupted interactions and provides a mechanistic explanation for the impact of mutations on key pathways.

Conclusions

Our results show the utility of our method both in identifying disruptions of protein interactions from known ligand binding site mutations, and in selecting novel clinically significant interactions.Supporting website with software and data: https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5532-5) contains supplementary material, which is available to authorized users.

Correlating topology and thermodynamics to predict protein structure sensitivity to point mutations

The relation between distribution of hydrophobic amino acids along with protein chains and their structure is far from being completely understood. No reliable method allowsab initioprediction of the folded structure from this distribution of physicochemical properties, even when they are highly degenerated by considering only two classes: hydrophobic and polar. Establishment of long-range hydrophobic three dimension (3D) contacts is essential for the formation of the nucleus, a key process in the early steps of protein folding. Thus, a large number of 3D simulation studies were developed to challenge this issue. They are nowadays evaluated in a specific chapter of the molecular modeling competition, Critical Assessment of Protein Structure Prediction. We present here a simulation of the early steps of the folding process for 850 proteins, performed in a discrete 3D space, which results in peaks in the predicted distribution of intra-chain noncovalent contacts. The residues located at these peak positions tend to be buried in the core of the protein and are expected to correspond to critical positions in the sequence, important both for folding and structural (or similarly, energetic in the thermodynamic hypothesis) stability. The degree of stabilization or destabilization due to a point mutation at the critical positions involved in numerous contacts is estimated from the calculated folding free energy difference between mutated and native structures. The results show that these critical positions are not tolerant towards mutation. This simulation of the noncovalent contacts only needs a sequence as input, and this paper proposes a validation of the method by comparison with the prediction of stability by well-established programs.