Mohammed Alshahrani

This study uses computational methods to analyze class 3 and class 4 antibodies' mechanisms of neutralizing SARS-CoV-2, revealing distinct strategies for binding, dynamics, and immune escape.

This paper analyzes the molecular mechanisms of broadly neutralizing SARS-CoV-2 antibodies by combining computational methods to reveal how conserved epitopes resist immune escape.

This study identifies how ultrapotent antibodies neutralize SARS-CoV-2 by distributing binding energy across rigid interfaces, enabling tolerance to mutations and informing therapeutic design.

This study uses computational methods to analyze how class 4 antibodies neutralize SARS-CoV-2 by targeting cryptic epitopes, revealing mechanisms involving allostery, binding energetics, and immune escape.

This study investigates how Class 4 and Class 5 antibodies modulate the conformational dynamics and immune escape of SARS-CoV-2 spike protein through allosteric effects and mutational scanning.

This study analyzes how different antibodies interact with SARS-CoV-2 spike protein using computational methods to identify binding hotspots and resistance mechanisms against variants.

This study identifies conserved allosteric hotspots in KRAS that mediate effector mimicry and allosteric modulation through molecular simulations and mutational analysis.

This study uses computational modeling to analyze binding mechanisms of four antibodies against SARS-CoV-2 Omicron variants, revealing distinct dynamic and evolutionary adaptations that influence immune escape.

This study identifies conserved allosteric hotspots in KRAS through computational analysis of interactions with monobodies and affimers, revealing structural and functional networks that govern binding and allostery.

This study identifies immune escape hotspots and binding determinants for broadly neutralizing antibodies against Omicron variants using molecular dynamics and deep mutational scanning.

This study adapts AlphaFold2 to predict conformational ensembles of allosteric ABL kinase mutants, showing that sequence masking and subsampling improve prediction of structural state shifts caused by mutations.

This study uses AlphaFold2 and molecular dynamics to predict conformational ensembles and binding mechanisms of SARS-CoV-2 Omicron variants, showing agreement with experimental data.

This study adapts AlphaFold2 to predict conformational ensembles and allosteric states of ABL kinase using sequence scanning and frustration profiling, revealing limitations in detecting low-populated inactive states.