Background: Molecular recognition is all pervasive in biology. Protein molecules are involved in enzyme regulation, immune response, signal transduction, oligomer assembly, etc. Delineation of physical and chemical features of the interface formed by protein-protein association would allow us to better understand protein interaction networks on one hand, and to design molecules that can engage a given interface and thereby control protein function on the other hand.
Results: ProFace is a suite of programs that uses a file, containing atomic coordinates of a multi-chain molecule, as input and analyzes the interface between any two or more subunits. The interface residues are shown segregated into spatial patches (if such a clustering is possible based on an input threshold distance) and/or core and rim regions. A number of physicochemical parameters defining the interface is tabulated. Among the different output files, one contains the list of interacting residues across the interface. Results can be used to infer if a particular interface belongs to a homodimeric molecule.
Conclusion: A web-server, ProFace (available at http://www.boseinst.ernet.in/resources/bioinfo/stag.html) has been developed for dissecting protein-protein interfaces and deriving various physicochemical parameters.
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http://dx.doi.org/10.1186/1472-6807-6-11 | DOI Listing |
Nat Rev Cancer
January 2025
Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, Atlanta, GA, USA.
Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs).
View Article and Find Full Text PDFBioeng Transl Med
January 2025
Polymeric micro- and nanoparticles are useful vehicles for delivering cytokines to diseased tissues such as solid tumors. Double emulsion solvent evaporation is one of the most common techniques to formulate cytokines into vehicles made from hydrophobic polymers; however, the liquid-liquid interfaces formed during emulsification can greatly affect the stability and therapeutic performance of encapsulated cytokines. To develop more effective cytokine-delivery systems, a clear molecular understanding of the interactions between relevant proteins and solvents used in the preparation of such particles is needed.
View Article and Find Full Text PDFMolecules
January 2025
Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF), University of Udine, 33100 Udine, Italy.
(1) Background: Electrostatics plays a capital role in protein-protein and protein-ligand interactions. Implicit solvent models are widely used to describe electrostatics and complementarity at interfaces. Electrostatic complementarity at the interface is not trivial, involving surface potentials rather than the charges of surfacial contacting atoms.
View Article and Find Full Text PDFFEBS J
January 2025
Physics, Department of Molecular and Translational Medicine, University of Brescia, Italy.
Neutrophil elastase (NE) is released by activated neutrophils during an inflammatory response and exerts proteolytic activity on elastin and other extracellular matrix components. This protease is rapidly inhibited by the plasma serine protease inhibitor alpha-1-antitrypsin (AAT), and the importance of this protective activity on lung tissue is highlighted by the development of early onset emphysema in individuals with AAT deficiency. As a serpin, AAT presents a surface-exposed reactive centre loop (RCL) whose sequence mirrors the target protease specificity.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.
Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a meta-learning framework designed to improve the adaptability of pretrained geometric models in such scenarios. To effectively boost the meta-learning optimization by injecting prior intersample distribution knowledge, three specially designed training sample cluster splitting patterns based on protein interaction interfaces are introduced.
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