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Protein aggregation is a complex process that strongly depends on environmental conditions and has considerable structural heterogeneity, not only at the level of fibril structure but also at the level of molecular oligomerization. Since the first step in aggregation is the formation of a dimer, it is important to clarify how certain properties (e.g., stability or the interface geometry) of the latter may determine the outcome of aggregation. Here, we developed a simple model that represents the dimer’s interfacial region by two angles (spanning the so-called growth landscape), and investigate how modulations of the interfacial region occurring on the ns–μs timescale change the dimer’s growth mode. We applied this methodology to 15 different dimer configurations of theβ2m D76N mutant protein equilibrated with long MD simulations and identified which of them have limited and unlimited growth modes, with different consequences to their aggregation potential. We found that despite the highly dynamic nature of the starting configurations, most polymeric growth modes tend to be conserved within the studied time scale. The proposed methodology performs remarkably well taking into consideration that theβ2m dimers are formed by monomers with detached termini, and their interfaces are stabilized by non-specific apolar interactions, leading to relatively weak binding affinities.

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein–ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein–ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and

The D76N mutant of the β 2 m protein is a biologically motivated model system to study protein aggregation. There is strong experimental evidence, supported by molecular simulations, that D76N populates a highly dynamic conformation (which we originally named I 2 ) that exposes aggregation-prone patches as a result of the detachment of the two terminal regions. Here, we use Molecular Dynamics simulations to study the stability of an ensemble of dimers of I 2 generated via protein–protein docking. MM-PBSA calculations indicate that within the ensemble of investigated dimers the major contribution to interface stabilization at physiological pH comes from hydrophobic interactions between apolar residues. Our structural analysis also reveals that the interfacial region associated with the most stable binding modes are particularly rich in residues pertaining to both the N- and C-terminus, as well residues from the BC- and DE-loops. On the other hand, the less stable interfaces are stabilized by intermolecular interactions involving residues from the CD- and EF-loops. By focusing on the most stable binding modes, we used a simple geometric rule to propagate the corresponding dimer interfaces. We found that, in the absence of any kind of structural rearrangement occurring at an early stage of the oligomerization pathway, some interfaces drive a self-limited growth process, while others can be propagated indefinitely allowing the formation of long, polymerized chains. In particular, the interfacial region of the most stable binding mode reported here falls in the class of self-limited growth.

S100B is an extracellular protein implicated in Alzheimer's Disease and a suppressor of amyloid-β aggregation. Herein we report a mechanism tying Cu2+ binding to a change in assembly state yielding disulfide cross-linked oligomers with higher anti-aggregation activity. This chemical control of chaperone function illustrates a regulatory process relevant under metal and proteostasis dysfunction as in neurodegeneration.

Graphical abstract, Highlights • β2m D76N mutant populates an aggregation-prone monomer (I2) with unstructured termini. • MD and MM-PBSA indicate that I2 dimers are stabilized by hydrophobic interactions. • The termini regions and BC- and DE-loops are prevalent in the most stable interfaces. • The most stable dimer has a limited growth potential without structural rearrangement., The D76N mutant of the β2m protein is a biologically motivated model system to study protein aggregation. There is strong experimental evidence, supported by molecular simulations, that D76N populates a highly dynamic conformation (which we originally named I2) that exposes aggregation-prone patches as a result of the detachment of the two terminal regions. Here, we use Molecular Dynamics simulations to study the stability of an ensemble of dimers of I2 generated via protein–protein docking. MM-PBSA calculations indicate that within the ensemble of investigated dimers the major contribution to interface stabilization at physiological pH comes from hydrophobic interactions between apolar residues. Our structural analysis also reveals that the interfacial region associated with the most stable binding modes are particularly rich in residues pertaining to both the N- and C-terminus, as well residues from the BC- and DE-loops. On the other hand, the less stable interfaces are stabilized by intermolecular interactions involving residues from the CD- and EF-loops. By focusing on the most stable binding modes, we used a simple geometric rule to propagate the corresponding dimer interfaces. We found that, in the absence of any kind of structural rearrangement occurring at an early stage of the oligomerization pathway, some interfaces drive a self-limited growth process, while others can be propagated indefinitely allowing the formation of long, polymerized chains. In particular, the interfacial region of the most stable binding mode reported here falls in the class of self-limited growth.

S100B is an extracellular protein implicated in Alzheimer's Disease and a suppressor of amyloid-β aggregation. Herein we report a mechanism tying Cu2+ binding to a change in assembly state yielding disulfide cross-linked oligomers with higher anti-aggregation activity. This chemical control of chaperone function illustrates a regulatory process relevant under metal and proteostasis dysfunction as in neurodegeneration.