Structure/function studies of HIV proteins HIV gp120 V3 loop modelling using de novo approaches HIV protease-inhibitor binding energy prediction

Structure/function studies of gp120 gp120 is missing loops (V1-3) in the experimental structure (1gc1) Re-build V3 loop in the context of the experimental structure Repeat for many different sequences for which phenotype is known Analyse ensembles of structures generated for patterns Correlate patterns to phenotype

Sequences modelled R5-specifying sequences: 1V02X15 CTRPNNNTRKSIQVGPGKAIYTTGEIIGDLRQAHC 1V10X P....R...A..G....I.K... 1V15PX S....R..A....R.F.A.DQ....I.K... 2V02PLX01 CTRPNNNTRKSIPIGPGRAFYATGEIIGDIRQAHC 2V07PLX T.R A...N V10PLX T Q V16PLX R..T QV..N V23X RHL.L DN v09x03 CTRPNNNTRKSI---NIGPGRAFYATGDIIGDIRQAHC 8v11px R..---T v13xb H A v17x H E Y. 8v24x H......S....E Y. 9V03x01 CTRPNNNTRKSIHIGPGRAWYTTGEIIG-DIRQAHC 9V09x QM.L V20x QM.L....H R.Y. 9V21x02.A..G......VQM.L....H X4-specifying sequences: CTRPNNNTRKSIQVGPGKAIYTTGEIIGDLRQAHC 1V10X S....R..A....R...A.EK....I.K... 1V15PX S....R..A....R.F.A.DK....I.K... 1V19PX S....R..A....R.F.A.DK....I.K... CTRPNNNTRKSIPIGPGRAFYATGEIIGDIRQAHC 2V07PLX H K V10PLX H EK...N V16PLX R......V...EK...N V23X RG.R......V...DK...N CTRPNNNTRKSI---NIGPGRAFYATGDIIGDIRQAHC 8v19px R.HIGH A......R.Y. 8v20x R.HIGH G......R.Y. 8v24x17....S.....R.RIGH G......R.Y. CTRPNNNTRKSIHIGPGRAWYTTGEIIG-DIRQAHC 9V21x S..G...RM.L...RH..R R.Y. 9V22x RH.G...RM.L....H..R K.Y. 9V25x SH.G...RM.L....H..R....G...K.Y. 9V25x RHAG...RM.M....H..R....D...K.YR

Semi-exhaustive segment-based folding EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK generate fragments from database 14-state ,  model …… minimise monte carlo with simulated annealing conformational space annealing, GA …… filter all-atom pairwise interactions, bad contacts compactness, secondary structure

HIV gp120 loop modelled in the presence of neutralising antibody

HIV gp120 loop ensembles

Current summary of gp120 related work Able to generate ensembles of loops that cluster for different sequences Able to match conformations generated with predictions made for human proteins with some statistical significance To do Need to use structure based alignments with other physical properties to correlate to sequences, and consequently the phenotype Not sure? “Ab binds to CD4 induced site on gp120” Blue sky: model interactions with models of receptors

Prediction of HIV-1 protease-inhibitor binding energies with MD MD simulation time Correlation coefficient ps with MD without MD Ekachai Jenwitheesuk

Molecular dynamics Force = -dU/dx (slope of potential U); acceleration, m a(t) = force All atoms are moving so forces between atoms are complicated functions of time Analytical solution for x(t) and v(t) is impossible; numerical solution is trivial Atoms move for very short times of seconds or picoseconds (ps) x(t+  t) = x(t) + v(t)  t + [4a(t) – a(t-  t)]  t 2 /6 v(t+  t) = v(t) + [2a(t+  t)+5a(t)-a(t-  t)]  t/6 U kinetic = ½ Σ m i v i (t) 2 = ½ n K B T Total energy (U potential + U kinetic ) must not change with time new position old position new velocity old velocity acceleration old velocity n is number of coordinates (not atoms)

Current summary of protease-inhibitor related work Able to predict binding energies with very good correlations to experimentally determined energies Framework for modelling binding interactions that incorporates flexibility of both protein and substrate To do/in progress Repeat for larger set of 100 protease-inhibitor complexes Relate binding energy to effectiveness of inhibitor Repeat for other proteins and inhibtors Incorporate into an HIV systems/population dynamics model