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Article ; Online: High-Throughput Interactome Determination via Sulfur Anomalous Scattering.

Miotto, Mattia / Milanetti, Edoardo / Mincigrucci, Riccardo / Masciovecchio, Claudio / Ruocco, Giancarlo

The journal of physical chemistry letters

2024 Volume 15, Issue 13, Page(s) 3478–3485

Abstract: We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and ... ...

Abstract	We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus, the differences in the patterns produced by bound proteins with respect to their nonbonded states can be exploited to rapidly assess protein complex formation. On the basis of our results, we suggest a possible experimental procedure for detecting protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale, thus paving the way for the development of a novel field of research based on a synchrotron light source.
MeSH term(s)	Crystallography, X-Ray ; Models, Molecular ; Proteins/chemistry ; Synchrotrons ; Sulfur/chemistry
Chemical Substances	Proteins ; Sulfur (70FD1KFU70)
Language	English
Publishing date	2024-03-21
Publishing country	United States
Document type	Journal Article
ISSN	1948-7185
ISSN (online)	1948-7185
DOI	10.1021/acs.jpclett.3c03632
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Article ; Online: Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer.

Desantis, Fausta / Miotto, Mattia / Milanetti, Edoardo / Ruocco, Giancarlo / Di Rienzo, Lorenzo

Proteins

2024

Abstract: Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of ... ...

Abstract	Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.26672
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Article ; Online: TOLOMEO, a Novel Machine Learning Algorithm to Measure Information and Order in Correlated Networks and Predict Their State.

Miotto, Mattia / Monacelli, Lorenzo

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 9

Abstract: We present ToloMEo (TOpoLogical netwOrk Maximum Entropy Optimization), a program implemented in C and Python that exploits a maximum entropy algorithm to evaluate network topological information. ToloMEo can study any system defined on a connected ... ...

Abstract	We present ToloMEo (TOpoLogical netwOrk Maximum Entropy Optimization), a program implemented in C and Python that exploits a maximum entropy algorithm to evaluate network topological information. ToloMEo can study any system defined on a connected network where nodes can assume N discrete values by approximating the system probability distribution with a Pottz Hamiltonian on a graph. The software computes entropy through a thermodynamic integration from the mean-field solution to the final distribution. The nature of the algorithm guarantees that the evaluated entropy is variational (i.e., it always provides an upper bound to the exact entropy). The program also performs machine learning, inferring the system's behavior providing the probability of unknown states of the network. These features make our method very general and applicable to a broad class of problems. Here, we focus on three different cases of study: (i) an agent-based model of a minimal ecosystem defined on a square lattice, where we show how topological entropy captures a crossover between hunting behaviors; (ii) an example of image processing, where starting from discretized pictures of cell populations we extract information about the ordering and interactions between cell types and reconstruct the most likely positions of cells when data are missing; and (iii) an application to recurrent neural networks, in which we measure the information stored in different realizations of the Hopfield model, extending our method to describe dynamical out-of-equilibrium processes.
Language	English
Publishing date	2021-08-31
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23091138
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Article: Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors.

Di Rienzo, Lorenzo / Miotto, Mattia / Milanetti, Edoardo / Ruocco, Giancarlo

Computational and structural biotechnology journal

2023 Volume 21, Page(s) 3002–3009

Abstract: Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. ... ...

Abstract	Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.
Language	English
Publishing date	2023-05-09
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.05.004
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Article ; Online: TOLOMEO, a Novel Machine Learning Algorithm to Measure Information and Order in Correlated Networks and Predict Their State

Mattia Miotto / Lorenzo Monacelli

Entropy, Vol 23, Iss 1138, p

2021 Volume 1138

Abstract	We present ToloMEo (TOpoLogical netwOrk Maximum Entropy Optimization), a program implemented in C and Python that exploits a maximum entropy algorithm to evaluate network topological information. ToloMEo can study any system defined on a connected network where nodes can assume N discrete values by approximating the system probability distribution with a Pottz Hamiltonian on a graph. The software computes entropy through a thermodynamic integration from the mean-field solution to the final distribution. The nature of the algorithm guarantees that the evaluated entropy is variational (i.e., it always provides an upper bound to the exact entropy). The program also performs machine learning, inferring the system’s behavior providing the probability of unknown states of the network. These features make our method very general and applicable to a broad class of problems. Here, we focus on three different cases of study: (i) an agent-based model of a minimal ecosystem defined on a square lattice, where we show how topological entropy captures a crossover between hunting behaviors; (ii) an example of image processing, where starting from discretized pictures of cell populations we extract information about the ordering and interactions between cell types and reconstruct the most likely positions of cells when data are missing; and (iii) an application to recurrent neural networks, in which we measure the information stored in different realizations of the Hopfield model, extending our method to describe dynamical out-of-equilibrium processes.
Keywords	entropy ; maximum entropy ; hopfield model ; machine learning ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	006
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
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Article ; Online: Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.

Grassmann, Greta / Miotto, Mattia / Desantis, Fausta / Di Rienzo, Lorenzo / Tartaglia, Gian Gaetano / Pastore, Annalisa / Ruocco, Giancarlo / Monti, Michele / Milanetti, Edoardo

Chemical reviews

2024 Volume 124, Issue 7, Page(s) 3932–3977

Abstract: Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable ...

Abstract	Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
MeSH term(s)	Humans ; Molecular Dynamics Simulation ; Proteins/chemistry ; Cell Communication ; Biophysical Phenomena
Chemical Substances	Proteins
Language	English
Publishing date	2024-03-27
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	207949-5
ISSN	1520-6890 ; 0009-2665
ISSN (online)	1520-6890
ISSN	0009-2665
DOI	10.1021/acs.chemrev.3c00550
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Article ; Online: Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors.

Milanetti, Edoardo / Miotto, Mattia / Bo', Leonardo / Di Rienzo, Lorenzo / Ruocco, Giancarlo

Chemico-biological interactions

2023 Volume 374, Page(s) 110380

Abstract: The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein ... ...

Abstract	The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.
MeSH term(s)	Humans ; SARS-CoV-2/metabolism ; Spike Glycoprotein, Coronavirus/metabolism ; COVID-19 ; Angiotensin-Converting Enzyme 2/metabolism ; Aptamers, Nucleotide/pharmacology ; Aptamers, Nucleotide/metabolism ; Protein Binding ; Peptidyl-Dipeptidase A/chemistry
Chemical Substances	spike protein, SARS-CoV-2 ; Spike Glycoprotein, Coronavirus ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23) ; Aptamers, Nucleotide ; Peptidyl-Dipeptidase A (EC 3.4.15.1)
Language	English
Publishing date	2023-02-21
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	218799-1
ISSN	1872-7786 ; 0009-2797
ISSN (online)	1872-7786
ISSN	0009-2797
DOI	10.1016/j.cbi.2023.110380
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Article ; Online: Dynamical changes of SARS-CoV-2 spike variants in the highly immunogenic regions impact the viral antibodies escaping.

Di Rienzo, Lorenzo / Miotto, Mattia / Desantis, Fausta / Grassmann, Greta / Ruocco, Giancarlo / Milanetti, Edoardo

Proteins

2023 Volume 91, Issue 8, Page(s) 1116–1129

Abstract: The prolonged circulation of the SARS-CoV-2 virus resulted in the emergence of several viral variants, with different spreading features. Moreover, the increased number of recovered and/or vaccinated people introduced a selective pressure toward variants ...

Abstract	The prolonged circulation of the SARS-CoV-2 virus resulted in the emergence of several viral variants, with different spreading features. Moreover, the increased number of recovered and/or vaccinated people introduced a selective pressure toward variants able to evade the immune system, developed against the former viral versions. This process results in reinfections. Aiming to study the latter process, we first collected a large structural dataset of antibodies in complex with the original version of SARS-CoV-2 Spike protein. We characterized the peculiarities of such antibodies population with respect to a control dataset of antibody-protein complexes, highlighting some statistically significant differences between these two sets of antibodies. Thus, moving our attention to the Spike side of the complexes, we identify the Spike region most prone to interaction with antibodies, describing in detail also the energetic mechanisms used by antibodies to recognize different epitopes. In this framework, fast protocols able to assess the effect of novel mutations on the cohort of developed antibodies would help establish the impact of the variants on the population. Performing a molecular dynamics simulation of the trimeric form of the SARS-CoV-2 Spike protein for the wild type and two variants of concern, that is, the Delta and Omicron variants, we described the physicochemical features and the conformational changes experienced locally by the variants with respect to the original version. Hence, combining the dynamical information with the structural study on the antibody-spike dataset, we quantitatively explain why the Omicron variant has a higher capability of escaping the immune system than the Delta variant, due to the higher conformational variability of the most immunogenic regions. Overall, our results shed light on the molecular mechanism behind the different responses the SARS-CoV-2 variants display against the immune response induced by either vaccines or previous infections. Moreover, our analysis proposes an approach that can be easily extended to both other SARS-CoV-2 variants or different molecular systems.
MeSH term(s)	Humans ; Antibodies, Viral ; SARS-CoV-2/genetics ; COVID-19 ; Antibodies, Neutralizing
Chemical Substances	Antibodies, Viral ; spike protein, SARS-CoV-2 ; Antibodies, Neutralizing
Language	English
Publishing date	2023-04-20
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.26497
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Book ; Online: High throughput interactome determination via sulfur anomalous scattering

Miotto, Mattia / Milanetti, Edoardo / Mincigrucci, Riccardo / Masciovecchio, Claudio / Ruocco, Giancarlo

2023

Abstract: We propose a novel approach to detect the binding between proteins making use of the anomalous diffraction of natively present heavy elements inside the molecule 3D structure. In particular, we suggest considering sulfur atoms contained in protein ... ...

Abstract	We propose a novel approach to detect the binding between proteins making use of the anomalous diffraction of natively present heavy elements inside the molecule 3D structure. In particular, we suggest considering sulfur atoms contained in protein structures at lower percentages than the other atomic species. Here, we run an extensive preliminary investigation to probe both the feasibility and the range of usage of the proposed protocol. In particular, we (i) analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus the differences in the patterns produced by bound proteins with respect to their non-bonded states can be exploited to rapidly assess protein complex formation. Next, we (ii) carried out analyses on the abundances of sulfurs in the different proteomes and molecular dynamics simulations on a representative set of protein structures to probe the typical motion of sulfur atoms. Finally, we (iii) suggest a possible experimental procedure to detect protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale even between other biological molecules, thus paving the way to the development of a novel field of research based on a synchrotron light source. Comment: 9 pages, 4 figures
Keywords	Physics - Biological Physics ; Quantitative Biology - Biomolecules
Subject code	612
Publishing date	2023-11-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides.

Parisi, Giacomo / Piacentini, Roberta / Incocciati, Alessio / Bonamore, Alessandra / Macone, Alberto / Rupert, Jakob / Zacco, Elsa / Miotto, Mattia / Milanetti, Edoardo / Tartaglia, Gian Gaetano / Ruocco, Giancarlo / Boffi, Alberto / Di Rienzo, Lorenzo

Frontiers in molecular biosciences

2024 Volume 10, Page(s) 1332359

Abstract: The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein ... ...

Abstract	The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein Interactions. Here, we applied a peptide design algorithm based on shape complementarity optimization and electrostatic compatibility and provided the first experimental
Language	English
Publishing date	2024-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2023.1332359
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