http://kth.diva-portal.org/smash/get/diva2:1303357/FULLTEXT01

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

/pdf/swiss-model-homology-modelling-of-protein-structures-and-dk4p9xtc0o.pdf

SWISS-MODEL: homology modelling of protein structures and complexes.

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

/pdf/swissadme-a-free-web-tool-to-evaluate-pharmacokinetics-drug-xebymauqho.pdf

SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks–Chandler–Andersen like integral method). We showcase the effectiveness of the tool ...

g_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations.

The BRAF kinase is attracting a lot of attention in oncology as alterations of its amino acid sequence can constitutively activate the MAP kinase signaling pathway, potentially contributing to the malignant transformation of the cell but at the same time rendering it sensitive to targeted therapy. Several pathologic BRAF variants were grouped in three different classes (I, II and III) based on their effects on the protein activity and pathway. Discerning the class of a BRAF mutation permits to adapt the treatment proposed to the patient. However, this information is lacking new and experimentally uncharacterized BRAF mutations detected in a patient biopsy. To overcome this issue, we developed a new in silico tool based on machine learning approaches to predict the potential class of a BRAF missense variant. As class I only involves missense mutations of Val600, we focused on the mutations of classes II and III, which are more diverse and challenging to predict. Using a logistic regression model and features including structural information, we were able to predict the classes of known mutations with an accuracy of 90%. This new and fast predictive tool will help oncologists to tackle potential pathogenic BRAF mutations and to propose the most appropriate treatment for their patients. 

/pdf/structure-based-prediction-of-braf-mutation-classes-using-2lnmepm9.pdf

Structure-based prediction of BRAF mutation classes using machine-learning approaches

Molecular docking is a computational approach for predicting the most probable position of a ligand in the binding site of a target macromolecule. Our docking algorithm Attracting Cavities (AC) has been shown to compare favorably to other widely used docking algorithms [Zoete, V.; et al. J. Comput. Chem. 2016, 37, 437]. Here we describe several improvements of AC, making the sampling more robust and providing more flexibility for either fast or high-accuracy docking. We benchmark the performance of AC 2.0 using the 285 complexes of the PDBbind Core set, version 2016. For redocking from randomized ligand conformations, AC 2.0 reaches a success rate of 73.3%, compared to 63.9% for GOLD and 58.0% for AutoDock Vina. Due to its force-field-based scoring function and its thorough sampling procedure, AC 2.0 also performs well for blind docking on the entire receptor surface. The accuracy of its scoring function allows for the detection of problematic experimental structures in the benchmark set. For cross-docking, the AC 2.0 success rate is about 30% lower than for redocking (42.5%), similar to GOLD (42.8%) and better than AutoDock Vina (33.1%), and it can be improved by an informed choice of flexible protein residues. For selected targets with a high success rate in cross-docking, AC 2.0 also achieves good enrichment factors in virtual screening.

Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking

The adoptive transfer of T cell receptor (TCR)-engineered T cells (ACT) targeting the HLA-A2 restricted cancer-testis epitope NY-ESO-1157-165 (A2/NY) has yielded favorable clinical responses against a variety of cancers. Two promising approaches to improve ACT efficacy are TCR affinity-optimization and combinatorial treatment strategies to reprogram the tumor microenvironment (TME). By computational design, we previously developed a panel of affinity-enhanced A2/NY-TCRs. Here, we have demonstrated improved tumor control and engraftment by T cells gene-modified to express one such TCR comprising a single amino acid replacement in CDR3β (A97L). To harness macrophages in the TME, we coengineered TCR-T cells to constitutively or inducibly secrete a high-affinity signal regulatory protein alpha (SiRPα) decoy (CV1) to block the CD47 ‘don’t eat me’ signal. We demonstrated better control of tumor outgrowth by CV1-Fc coengineered TCR-T cells but in subcutaneous xenograft tumor models we observed depletion of both CV1-Fc and CV1 coengineered T cells. Importantly, CV1 coengineered T cells were not depleted by human macrophages in vitro. Moreover, Avelumab and Cetuximab enhanced macrophage-mediated phagocytosis in vitro in the presence of CV1, and augmented tumor control upon ACT. Taken together, our study indicates important clinical promise for harnessing macrophages by combining CV1 coengineered TCR-T cells with tumor-targeting monoclonal antibodies.

https://www.biorxiv.org/content/biorxiv/early/2023/06/29/2023.06.27.546523.full.pdf

Combination of NY-ESO-1-TCR-T-cells coengineered to secrete SiRPα decoys with anti-tumor antibodies to augment macrophage phagocytosis

While certain residues have clear involvement in determining the 3D structure of a macromolecule because they affect the folding topology or the overall protein stability, the role of different residues in ligand accommodation and binding has attracted less attention. On the basis of the assumption that drug-binding sites on target molecules have specific amino acid compositions, the incidence of each standard amino acid at the binding sites of small molecules and their correlations are calculated for an unprecedented large set of high-quality X-ray structures. Results show, for the first time, strong and highly correlated enrichments of aromatic and sulfur-containing residues, which play an important role in ligand binding and shape the nature of the chemical interactions.

Strong Enrichment of Aromatic and Sulfur-Containing Residues in Ligand-Protein Binding Sites.

The pesent invention relates to compounds of formula (I) pharmaceutically-acceptable salts, hydrates, solvates, or stereoisomers thereof and their use for the prevention and treatment of oncovirus induced cancer in a subject.

Vincent Zoete

Papers

Structure-based prediction of BRAF mutation classes using machine-learning approaches

Attracting Cavities 2.0: Improving the Flexibility and Robustness for Small-Molecule Docking

Combination of NY-ESO-1-TCR-T-cells coengineered to secrete SiRPα decoys with anti-tumor antibodies to augment macrophage phagocytosis

Strong Enrichment of Aromatic and Sulfur-Containing Residues in Ligand-Protein Binding Sites.

Compounds for the treatment of oncovirus induced cancer and methods of use thereof